2025
He, Zhixiang; Ran, Fengyuan; Chen, Jing; Gu, Yangyang; He, Kun; Du, Ruiying; Jia, Ju; Wu, Cong
HT-Auth: Secure VR Headset Authentication via Subtle Head Tremors Journal Article
In: Proc. {ACM} Interact. Mob. Wearable Ubiquitous Technol, vol. 9, pp. 85:1–85:26, 2025.
@article{He2025,
title = {HT-Auth: Secure VR Headset Authentication via Subtle Head Tremors},
author = {Zhixiang He and Fengyuan Ran and Jing Chen and Yangyang Gu and Kun He and Ruiying Du and Ju Jia and Cong Wu},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2025/09/3749980-1.pdf},
doi = {10.1145/3749980},
year = {2025},
date = {2025-09-13},
journal = {Proc. {ACM} Interact. Mob. Wearable Ubiquitous Technol},
volume = {9},
pages = {85:1--85:26},
abstract = {While Virtual Reality (VR) applications have gained popularity in recent years, efficiently identifying users on VR devices remains challenging. Current solutions, such as passwords and digital PINs, relying on handheld controllers or in-air hand gestures, are time-consuming and far less convenient than typing on touchscreens or physical keyboards. Even worse, the entry process can be observed by others in proximity, raising security concerns. In this paper, we propose HT-Auth, a novel authentication method for VR devices based on subtle head tremors. These tremors, occurring during active force exertion, are intrinsic and inevitable for human beings, which can be easily captured by inertial sensors built-in commodity VR headsets. We thus derive neck muscular biometrics from the tremor signal for secure VR device authentication. Our experiments, conducted with both standalone and mobile VR headsets, achieve a commendable balanced accuracy of 97.22% with just 10 registration samples, proving its efficacy and resilience against potential threats. Our source code is available at https://anonymous.4open.science/r/HT-OpenSource-10C3/.
},
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pubstate = {published},
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While Virtual Reality (VR) applications have gained popularity in recent years, efficiently identifying users on VR devices remains challenging. Current solutions, such as passwords and digital PINs, relying on handheld controllers or in-air hand gestures, are time-consuming and far less convenient than typing on touchscreens or physical keyboards. Even worse, the entry process can be observed by others in proximity, raising security concerns. In this paper, we propose HT-Auth, a novel authentication method for VR devices based on subtle head tremors. These tremors, occurring during active force exertion, are intrinsic and inevitable for human beings, which can be easily captured by inertial sensors built-in commodity VR headsets. We thus derive neck muscular biometrics from the tremor signal for secure VR device authentication. Our experiments, conducted with both standalone and mobile VR headsets, achieve a commendable balanced accuracy of 97.22% with just 10 registration samples, proving its efficacy and resilience against potential threats. Our source code is available at https://anonymous.4open.science/r/HT-OpenSource-10C3/.
Haolin, Wu; Chang, Liu; Jing, Chen; Ruiying, Du; Kun, He; Yu, Zhang; Cong, Wu; Tianwei, Zhang; Qing, Guo; Jie, Zhang
When Translators Refuse to Translate: A Novel Attack to Speech Translation Systems Proceedings Article
In: 34th USENIX Security Symposium (USENIX Security 25), pp. 4723–4740, USENIX Association, Seattle, WA, USA, 2025.
@inproceedings{nokey,
title = {When Translators Refuse to Translate: A Novel Attack to Speech Translation Systems},
author = {Wu Haolin and Liu Chang and Chen Jing and Du Ruiying and He Kun and Zhang Yu and Wu Cong and Zhang Tianwei and Guo Qing and Zhang Jie},
url = {https://www.usenix.org/system/files/usenixsecurity25-wu-haolin.pdf
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/usenixsecurity25-wu-haolin-1.pdf},
year = {2025},
date = {2025-08-13},
booktitle = {34th USENIX Security Symposium (USENIX Security 25)},
pages = {4723--4740},
publisher = {USENIX Association},
address = {Seattle, WA, USA},
abstract = {Speech translation, which converts a spoken language into another spoken or written language, has experienced rapid advance recently. However, the security in this domain remains underexplored. In this work, we uncover a novel security threat unique to speech translation systems, which is dubbed "untranslation attack". We observe that state-of-the-art (SOTA) models, despite their strong translation capabilities, exhibit an inherent tendency to output the content in the source speech language rather than the desired target language. Leveraging this phenomenon, we propose an attack model that deceives the system into outputting the source language content instead of translating it. Interestingly, we find that this approach achieves significant attack effectiveness with minimal overhead compared to traditional semantic perturbation attacks: it achieves a high attack success rate of 87.5% with a perturbation budget of as low as 0.001. Furthermore, we extend this approach to develop a universal perturbation attack, successfully testing it in the physical world.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Speech translation, which converts a spoken language into another spoken or written language, has experienced rapid advance recently. However, the security in this domain remains underexplored. In this work, we uncover a novel security threat unique to speech translation systems, which is dubbed "untranslation attack". We observe that state-of-the-art (SOTA) models, despite their strong translation capabilities, exhibit an inherent tendency to output the content in the source speech language rather than the desired target language. Leveraging this phenomenon, we propose an attack model that deceives the system into outputting the source language content instead of translating it. Interestingly, we find that this approach achieves significant attack effectiveness with minimal overhead compared to traditional semantic perturbation attacks: it achieves a high attack success rate of 87.5% with a perturbation budget of as low as 0.001. Furthermore, we extend this approach to develop a universal perturbation attack, successfully testing it in the physical world.
Nan, Yan; Yuqing, Li; Xiong, Wang; Jing, Chen; Kun, He; Bo, Li
EmbedX: Embedding-Based Cross-Trigger Backdoor Attack Against Large Language Models Proceedings Article
In: 34th USENIX Security Symposium (USENIX Security 25), pp. 241–257, USENIX Association, Seattle, WA, USA, 2025.
@inproceedings{nokey,
title = {EmbedX: Embedding-Based Cross-Trigger Backdoor Attack Against Large Language Models},
author = {Yan Nan and Li Yuqing and Wang Xiong and Chen Jing and He Kun and Li Bo},
url = {https://www.usenix.org/system/files/usenixsecurity25-yan-nan.pdf
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/usenixsecurity25-yan-nan-1.pdf},
year = {2025},
date = {2025-08-13},
urldate = {2025-08-13},
booktitle = {34th USENIX Security Symposium (USENIX Security 25)},
journal = {IEEE Transactions on Information Forensics and Security},
pages = {241--257},
publisher = {USENIX Association},
address = {Seattle, WA, USA},
abstract = {Large language models (LLMs) nowadays have attracted an affluent user base due to the superior performance across various downstream tasks. Yet, recent works reveal that LLMs are vulnerable to backdoor attacks, where an attacker can inject a specific token trigger to manipulate the model\'s behaviors during inference. Existing efforts have largely focused on single-trigger attacks while ignoring the variations in different users\' responses to the same trigger, thus often resulting in undermined attack effectiveness. In this work, we propose EmbedX, an effective and efficient cross-trigger backdoor attack against LLMs. Specifically, EmbedX exploits the continuous embedding vector as the soft trigger for backdooring LLMs, which enables trigger optimization in the semantic space. By mapping multiple tokens into the same soft trigger, EmbedX establishes a backdoor pathway that links these tokens to the attacker\'s target output. To ensure the stealthiness of EmbedX, we devise a latent adversarial backdoor mechanism with dual constraints in frequency and gradient domains, which effectively crafts the poisoned samples close to the target samples. Through extensive experiments on four popular LLMs across both classification and generation tasks, we show that EmbedX achieves the attack goal effectively, efficiently, and stealthily while also preserving model utility.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Large language models (LLMs) nowadays have attracted an affluent user base due to the superior performance across various downstream tasks. Yet, recent works reveal that LLMs are vulnerable to backdoor attacks, where an attacker can inject a specific token trigger to manipulate the model's behaviors during inference. Existing efforts have largely focused on single-trigger attacks while ignoring the variations in different users' responses to the same trigger, thus often resulting in undermined attack effectiveness. In this work, we propose EmbedX, an effective and efficient cross-trigger backdoor attack against LLMs. Specifically, EmbedX exploits the continuous embedding vector as the soft trigger for backdooring LLMs, which enables trigger optimization in the semantic space. By mapping multiple tokens into the same soft trigger, EmbedX establishes a backdoor pathway that links these tokens to the attacker's target output. To ensure the stealthiness of EmbedX, we devise a latent adversarial backdoor mechanism with dual constraints in frequency and gradient domains, which effectively crafts the poisoned samples close to the target samples. Through extensive experiments on four popular LLMs across both classification and generation tasks, we show that EmbedX achieves the attack goal effectively, efficiently, and stealthily while also preserving model utility.
Zhou, Erjun; Chen, Jing; Shi, Min; Huang, Zhengdi; Jia, Meng; He, Kun; Du, Ruiying
Boreas: Fully Anonymous Sealed-Bid Auction Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, pp. 8509 - 8524, 2025.
@article{Zhou2024,
title = {Boreas: Fully Anonymous Sealed-Bid Auction},
author = {Erjun Zhou and Jing Chen and Min Shi and Zhengdi Huang and Meng Jia and Kun He and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Boreas_Fully_Anonymous_Sealed-Bid_Auction_compressed.pdf},
doi = {10.1109/TIFS.2025.3593063},
year = {2025},
date = {2025-07-28},
urldate = {2025-07-28},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
pages = {8509 - 8524},
abstract = {With the rise of e-commerce, sealed-bid auctions are widely used in various online scenarios. In auctions, bidders’ bids and participants’ identities are considered critical private information. However, existing works either only achieve bid privacy or fail to provide complete protection of identity. In this work, we propose the first sealed-bid auction scheme that achieves both bid privacy and identity privacy, called Boreas. We propose three fundamental protocols as the building blocks. In particular, anonymous submission enables sellers to submit items anonymously, oblivious bidding and locker transaction enable the seller and the winner to confirm the auction results and complete the transaction without knowing each other’s identity. Meanwhile, we formally define the security goal of identity privacy and formalize a new security property called: fully anonymous. We prove the security of our scheme in the semi-honest adversary model. We implement Boreas and run experiments comparing its performance against existing schemes. Our experiments show that Boreas improves computation time by 12.6% and reduces communication costs by 103× in handling a large-scale auction, while offering stronger security guarantee.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the rise of e-commerce, sealed-bid auctions are widely used in various online scenarios. In auctions, bidders’ bids and participants’ identities are considered critical private information. However, existing works either only achieve bid privacy or fail to provide complete protection of identity. In this work, we propose the first sealed-bid auction scheme that achieves both bid privacy and identity privacy, called Boreas. We propose three fundamental protocols as the building blocks. In particular, anonymous submission enables sellers to submit items anonymously, oblivious bidding and locker transaction enable the seller and the winner to confirm the auction results and complete the transaction without knowing each other’s identity. Meanwhile, we formally define the security goal of identity privacy and formalize a new security property called: fully anonymous. We prove the security of our scheme in the semi-honest adversary model. We implement Boreas and run experiments comparing its performance against existing schemes. Our experiments show that Boreas improves computation time by 12.6% and reduces communication costs by 103× in handling a large-scale auction, while offering stronger security guarantee.
Wang, Beining; Li, Yinuo; Chen, Jing; He, Kun; Jia, Meng; Du, Ruiying
Forward and Backward Private Conjunctive Dynamic Searchable Symmetric Encryption With Refined Leakage Function and Low Communication Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, pp. 8224 - 8236, 2025.
@article{nokey,
title = {Forward and Backward Private Conjunctive Dynamic Searchable Symmetric Encryption With Refined Leakage Function and Low Communication},
author = {Beining Wang and Yinuo Li and Jing Chen and Kun He and Meng Jia and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Forward_and_Backward_Private_Conjunctive_Dynamic_Searchable_Symmetric_Encryption_With_Refined_Leakage_Function_and_Low_Communication-1.pdf},
doi = {10.1109/TIFS.2025.3592533},
year = {2025},
date = {2025-07-24},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
pages = {8224 - 8236},
abstract = {Dynamic searchable symmetric encryption (DSSE) enables updates and keyword searches on outsourced encrypted data while minimizing the information revealed to the server. However, existing DSSE schemes that support conjunctive keyword searches disclose added documents or fail to filter deleted ones in certain circumstances, thus violating forward and backward privacy. Besides, the size of their search tokens increases with the number of documents, which incurs a heavy communication cost. In this paper, we develop a conjunctive DSSE scheme that has a search token size only related to the conjunction size and fully supports forward and backward privacy. Our scheme is based on a new three-dimensional chain structure called CUBE. We also rethink the leakage function of conjunctive queries and prove that our scheme satisfies the refined security definition. Experimental results demonstrate that compared with the state-of-the-art schemes, our scheme increases the computational cost by at most 9.62% but reduces the communication cost by 99.78% when searching six conjunctive keywords.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Dynamic searchable symmetric encryption (DSSE) enables updates and keyword searches on outsourced encrypted data while minimizing the information revealed to the server. However, existing DSSE schemes that support conjunctive keyword searches disclose added documents or fail to filter deleted ones in certain circumstances, thus violating forward and backward privacy. Besides, the size of their search tokens increases with the number of documents, which incurs a heavy communication cost. In this paper, we develop a conjunctive DSSE scheme that has a search token size only related to the conjunction size and fully supports forward and backward privacy. Our scheme is based on a new three-dimensional chain structure called CUBE. We also rethink the leakage function of conjunctive queries and prove that our scheme satisfies the refined security definition. Experimental results demonstrate that compared with the state-of-the-art schemes, our scheme increases the computational cost by at most 9.62% but reduces the communication cost by 99.78% when searching six conjunctive keywords.
Jia, Meng; Chen, Jing; Wang, Yuanzheng; He, Kun; Shi, Min; Du, Ruiying
Multi-Authority Anonymous Credentials With Efficient and Decentralized Supervision Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, pp. 8154 - 8166, 2025.
@article{nokey,
title = {Multi-Authority Anonymous Credentials With Efficient and Decentralized Supervision},
author = {Meng Jia and Jing Chen and Yuanzheng Wang and Kun He and Min Shi and Ruiying Du},
url = {https://ieeexplore.ieee.org/document/11095743},
doi = {10.1109/TIFS.2025.3592554},
year = {2025},
date = {2025-07-24},
urldate = {2025-07-24},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
pages = {8154 - 8166},
abstract = {Anonymous credential is widely used in online services, where issuers in authorities issue credentials to users and then users can selectively and privately prove their identities and attributes. However, users may misbehave under anonymous settings. Therefore, we need to trace the credential proof to obtain the user’s identity and link credential proofs to achieve supervision. Existing solutions either have the single point of failure problem or require multiple supervisors perform threshold computations on all users’ identities, it is inefficient in practice especially when the number of users increases. In this paper, we present a credential management system in multiple authorities with efficient and decentralized supervision. Specifically, we design a multi-authority credential management architecture, where each issuer in authorities issues credentials to users and supervisors trace and link credential proofs in multiple authorities. Then, we present efficient and decentralized credential proof tracing and linking protocols, where more than threshold supervisors can trace credential proofs to obtain users’ identities and generate users’ linking keys. Verifiers can link each malicious user’s credential proofs efficiently with those linking keys. We conduct experiments on our system in the WAN and LAN settings and compare it with another threshold attribute-based credential scheme. The experimental results demonstrate that our solution is efficient in practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Anonymous credential is widely used in online services, where issuers in authorities issue credentials to users and then users can selectively and privately prove their identities and attributes. However, users may misbehave under anonymous settings. Therefore, we need to trace the credential proof to obtain the user’s identity and link credential proofs to achieve supervision. Existing solutions either have the single point of failure problem or require multiple supervisors perform threshold computations on all users’ identities, it is inefficient in practice especially when the number of users increases. In this paper, we present a credential management system in multiple authorities with efficient and decentralized supervision. Specifically, we design a multi-authority credential management architecture, where each issuer in authorities issues credentials to users and supervisors trace and link credential proofs in multiple authorities. Then, we present efficient and decentralized credential proof tracing and linking protocols, where more than threshold supervisors can trace credential proofs to obtain users’ identities and generate users’ linking keys. Verifiers can link each malicious user’s credential proofs efficiently with those linking keys. We conduct experiments on our system in the WAN and LAN settings and compare it with another threshold attribute-based credential scheme. The experimental results demonstrate that our solution is efficient in practice.
Liang, Ruichao; Chen, Jing; Cao, Ruochen; He, Kun; Du, Ruiying; Li, Shuhua; Lin, Zheng; Wu, Cong
SmartShot: Hunt Hidden Vulnerabilities in Smart Contracts using Mutable Snapshots Proceedings Article
In: 33rd FSE 2025, pp. 65-85, ACM, Trondheim, Norway, 2025.
@inproceedings{nokey,
title = {SmartShot: Hunt Hidden Vulnerabilities in Smart Contracts using Mutable Snapshots},
author = {Ruichao Liang and Jing Chen and Ruochen Cao and Kun He and Ruiying Du and Shuhua Li and Zheng Lin and Cong Wu},
url = {https://dl.acm.org/doi/pdf/10.1145/3715714
},
doi = {10.1145/3715714},
year = {2025},
date = {2025-07-19},
urldate = {2025-07-19},
booktitle = {33rd FSE 2025},
pages = {65-85},
publisher = {ACM},
address = {Trondheim, Norway},
abstract = {Smart contracts, as Turing-complete programs managing billions of assets in decentralized finance, are prime targets for attackers. While fuzz testing seems effective for detecting vulnerabilities in these programs, we identify several significant challenges when targeting smart contracts: (i) the stateful nature of these contracts requires stateful exploration, but current fuzzers rely on transaction sequences to manipulate contract states, making the process inefficient; (ii) contract execution is influenced by the continuously changing blockchain environment, yet current fuzzers are limited to local deployments, failing to test contracts in real-world scenarios. These challenges hinder current fuzzers from uncovering hidden vulnerabilities, i.e., those concealed in deep contract states and specific blockchain environments. In this paper, we present SmartShot, a mutable snapshot-based fuzzer to hunt hidden vulnerabilities within smart contracts. We innovatively formulate contract states and blockchain environments as directly fuzzable elements and design mutable snapshots to quickly restore and mutate these elements. SmartShot features a symbolic taint analysis-based mutation strategy along with double validation to soundly guide the state mutation. SmartShot mutates blockchain environments using contract’s historical on-chain states, providing real-world execution contexts. We propose a snapshot checkpoint mechanism to integrate mutable snapshots into SmartShot’s fuzzing loops. These innovations enable SmartShot to effectively fuzz contract states, test contracts across varied and realistic blockchain environments, and support on-chain fuzzing. Experimental results show that SmartShot is effective to detect hidden vulnerabilities with the highest code coverage and lowest false positive rate. SmartShot is 4.8× to 20.2× faster than state-of-the-art tools, identifying 2,150 vulnerable contracts out of 42,738 real-world contracts which is 2.1× to 13.7× more than other tools. SmartShot has demonstrated its real-world impact by detecting vulnerabilities that are only discoverable on-chain and uncovering 24 0-day vulnerabilities in the latest 10,000 deployed contracts.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Smart contracts, as Turing-complete programs managing billions of assets in decentralized finance, are prime targets for attackers. While fuzz testing seems effective for detecting vulnerabilities in these programs, we identify several significant challenges when targeting smart contracts: (i) the stateful nature of these contracts requires stateful exploration, but current fuzzers rely on transaction sequences to manipulate contract states, making the process inefficient; (ii) contract execution is influenced by the continuously changing blockchain environment, yet current fuzzers are limited to local deployments, failing to test contracts in real-world scenarios. These challenges hinder current fuzzers from uncovering hidden vulnerabilities, i.e., those concealed in deep contract states and specific blockchain environments. In this paper, we present SmartShot, a mutable snapshot-based fuzzer to hunt hidden vulnerabilities within smart contracts. We innovatively formulate contract states and blockchain environments as directly fuzzable elements and design mutable snapshots to quickly restore and mutate these elements. SmartShot features a symbolic taint analysis-based mutation strategy along with double validation to soundly guide the state mutation. SmartShot mutates blockchain environments using contract’s historical on-chain states, providing real-world execution contexts. We propose a snapshot checkpoint mechanism to integrate mutable snapshots into SmartShot’s fuzzing loops. These innovations enable SmartShot to effectively fuzz contract states, test contracts across varied and realistic blockchain environments, and support on-chain fuzzing. Experimental results show that SmartShot is effective to detect hidden vulnerabilities with the highest code coverage and lowest false positive rate. SmartShot is 4.8× to 20.2× faster than state-of-the-art tools, identifying 2,150 vulnerable contracts out of 42,738 real-world contracts which is 2.1× to 13.7× more than other tools. SmartShot has demonstrated its real-world impact by detecting vulnerabilities that are only discoverable on-chain and uncovering 24 0-day vulnerabilities in the latest 10,000 deployed contracts.
Yang, Hao; Chen, Jing; Pan, Kewen; He, Kun; Jia, Meng; Du, Ruiying
Volia: An Efficient and Light Asynchronous BFT Protocol Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, 2025.
@article{Yang2025,
title = {Volia: An Efficient and Light Asynchronous BFT Protocol},
author = {Hao Yang and Jing Chen and Kewen Pan and Kun He and Meng Jia and Ruiying Du},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=11039806
},
doi = {10.1109/TIFS.2025.3581055},
year = {2025},
date = {2025-06-18},
urldate = {2025-06-18},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
abstract = {Byzantine Fault Tolerance (BFT) protocols can be divided into synchronous BFT protocols, partially synchronous BFT protocols, and asynchronous BFT protocols according to communication delay. Asynchronous BFT protocols are widely used because they can tolerate uncertain communication delays in the real world. However, asynchronous BFT protocols need to perform many rounds of broadcasts to reach agreement on a transaction subset, which consumes a lot of communication, computing, and storage resources. In this paper, we present Volia, an asynchronous BFT protocol which resolves above problem. We design new broadcast protocol to reduce the number of broadcast rounds needed for agreement. It reduces the communication overhead. Voting broadcast is used to maintain the order of transaction subsets rather than threshold signature to reduce computation cost. Above mechanisms speeds up the agreement phase, reduces the accumulated transaction subsets waiting for agreement and thus saves storage resources. We conduct experiment on Volia and the results show that Volia exhibits about 2∼65× throughput, 2∼25 % latency, and 30% storage cost compared to other asynchronous BFT protocols.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Byzantine Fault Tolerance (BFT) protocols can be divided into synchronous BFT protocols, partially synchronous BFT protocols, and asynchronous BFT protocols according to communication delay. Asynchronous BFT protocols are widely used because they can tolerate uncertain communication delays in the real world. However, asynchronous BFT protocols need to perform many rounds of broadcasts to reach agreement on a transaction subset, which consumes a lot of communication, computing, and storage resources. In this paper, we present Volia, an asynchronous BFT protocol which resolves above problem. We design new broadcast protocol to reduce the number of broadcast rounds needed for agreement. It reduces the communication overhead. Voting broadcast is used to maintain the order of transaction subsets rather than threshold signature to reduce computation cost. Above mechanisms speeds up the agreement phase, reduces the accumulated transaction subsets waiting for agreement and thus saves storage resources. We conduct experiment on Volia and the results show that Volia exhibits about 2∼65× throughput, 2∼25 % latency, and 30% storage cost compared to other asynchronous BFT protocols.
Wu, Cong; He, Kun; Chen, Jing; Du, Ruiying; Yan, Ran; Zhao, Ziming
High Accuracy and Presentation Attack Resistant Hand Authentication via Acoustic Sensing for Commodity Mobile Devices Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 22, pp. 5231-5247, 2025.
@article{nokey,
title = {High Accuracy and Presentation Attack Resistant Hand Authentication via Acoustic Sensing for Commodity Mobile Devices},
author = {Cong Wu and Kun He and Jing Chen and Ruiying Du and Ran Yan and Ziming Zhao},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10979357
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/High_Accuracy_and_Presentation_Attack_Resistant_Hand_Authentication_via_Acoustic_Sensing_for_Commodity_Mobile_Devices-已壓縮-1_compressed-1.pdf},
doi = {10.1109/TDSC.2025.3564408},
year = {2025},
date = {2025-04-28},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {22},
pages = {5231-5247},
abstract = {Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. Existing hand authentication schemes, however, use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with an active acoustic sensing of the other hand. To this end, EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability, security, and usability of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks. The results of the user study also suggest that the required hand gestures are easy to perform, and EchoHand is very user-friendly with low latency.},
keywords = {},
pubstate = {published},
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Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. Existing hand authentication schemes, however, use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with an active acoustic sensing of the other hand. To this end, EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability, security, and usability of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks. The results of the user study also suggest that the required hand gestures are easy to perform, and EchoHand is very user-friendly with low latency.
Sun, Jianfei; Xu, Guowen; Yang, Yang; Yang, Xuehuan; Li, Xiaoguo; Wu, Cong; Liu, Zhen; Yang, Guomin; Deng, Robert H.
Forward-Secure Hierarchical Delegable Signature for Smart Homes Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, pp. 3950-3965, 2025.
@article{nokey,
title = {Forward-Secure Hierarchical Delegable Signature for Smart Homes},
author = {Jianfei Sun and Guowen Xu and Yang Yang and Xuehuan Yang and Xiaoguo Li and Cong Wu and Zhen Liu and Guomin Yang and Robert H. Deng},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10942402
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Forward-Secure_Hierarchical_Delegable_Signature_for_Smart_Homes.pdf},
doi = {10.1109/TIFS.2025.3555185},
year = {2025},
date = {2025-03-26},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
pages = {3950-3965},
abstract = {Aiming to provide people with great convenience and comfort, smart home systems have been deployed in thousands of homes. In this paper, we focus on handling the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: 1) fine-grained, privacy-preserving authorization for smart home users and integrity protection of communication contents; 2) flexible self-sovereign permission delegation; 3) forward security of previous messages. To our knowledge, no previous system has been designed to consider these three security and privacy requirements simultaneously. To tackle these challenges, we put forward the first-ever efficient cryptographic primitive called the Forward-secure Hierarchical Delegable Signature (FS-HDS) scheme for smart homes. Specifically, we first propose a new primitive, efficient Hierarchical Delegable Signature (HDS) scheme, which is capable of supporting partial delegation capability while realizing privacy-preserving authorization and integrity guarantee. Then, we present an FS-HDS for smart homes with the efficient HDS as the underlying building block, which not only inherits all the desirable features of HDS but also ensures that the past content integrity is not affected even if the current secret key is compromised. We provide comprehensively strict security proofs to prove the security of our proposed solutions. Its performance is also validated via experimental simulations to showcase its practicability and effectiveness.},
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Aiming to provide people with great convenience and comfort, smart home systems have been deployed in thousands of homes. In this paper, we focus on handling the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: 1) fine-grained, privacy-preserving authorization for smart home users and integrity protection of communication contents; 2) flexible self-sovereign permission delegation; 3) forward security of previous messages. To our knowledge, no previous system has been designed to consider these three security and privacy requirements simultaneously. To tackle these challenges, we put forward the first-ever efficient cryptographic primitive called the Forward-secure Hierarchical Delegable Signature (FS-HDS) scheme for smart homes. Specifically, we first propose a new primitive, efficient Hierarchical Delegable Signature (HDS) scheme, which is capable of supporting partial delegation capability while realizing privacy-preserving authorization and integrity guarantee. Then, we present an FS-HDS for smart homes with the efficient HDS as the underlying building block, which not only inherits all the desirable features of HDS but also ensures that the past content integrity is not affected even if the current secret key is compromised. We provide comprehensively strict security proofs to prove the security of our proposed solutions. Its performance is also validated via experimental simulations to showcase its practicability and effectiveness.
Yan, Ran; Du, Ruiying; He, Kun; Chen, Jing; Li, Qiao; Wu, Cong
Universal and Efficient Adversarial Training Framework with Membership Inference Resistance Journal Article
In: IEEE Internet of Things Journal, vol. 12, pp. 18665-18677, 2025.
@article{nokey,
title = {Universal and Efficient Adversarial Training Framework with Membership Inference Resistance},
author = {Ran Yan and Ruiying Du and Kun He and Jing Chen and Qiao Li and Cong Wu},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10929713
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Universal_and_Efficient_Adversarial_Training_Framework_With_Membership_Inference_Resistance.pdf
},
doi = {10.1109/JIOT.2025.3551762},
year = {2025},
date = {2025-03-17},
journal = {IEEE Internet of Things Journal},
volume = {12},
pages = {18665-18677},
abstract = {Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this article, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime.},
keywords = {},
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Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this article, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime.
He, Zhixiang; Chen, Jing; He, Kun; Gu, Yangyang; Deng, Qiyi; Zhang, Zijian; Du, Ruiying; Zhao, Qingchuan; Wu, Cong
HeadSonic: Usable Bone Conduction Earphone Authentication via Head-conducted Sounds Journal Article
In: IEEE Transactions on Mobile Computing , vol. 24, pp. 7914-7928, 2025.
@article{nokey,
title = {HeadSonic: Usable Bone Conduction Earphone Authentication via Head-conducted Sounds},
author = {Zhixiang He and Jing Chen and Kun He and Yangyang Gu and Qiyi Deng and Zijian Zhang and Ruiying Du and Qingchuan Zhao and Cong Wu},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10925832
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Universal_and_Efficient_Adversarial_Training_Framework_With_Membership_Inference_Resistance-1.pdf},
doi = {10.1109/TMC.2025.3551272},
year = {2025},
date = {2025-03-13},
journal = {IEEE Transactions on Mobile Computing },
volume = {24},
pages = {7914-7928},
abstract = {Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentication methods are all specifically designed for air-conduction earphones, which are not suited for bone conduction earphones (BCEs) that rely on bone conduction mechanisms. In this paper, we propose HeadSonic, a usable BCE authentication system based on the unique head-conducted sounds, which can be acquired when the user wears the BCE device. Specifically, the system emits a millisecond-level sound to initiate the authentication session. The signal captured by the BCE microphone is propagated through the user\'s head, which is unique in density, geometry, and bone-tissue ratio. It operates implicitly, while maintaining robustness across different behaviors. Extensive experiments involving 60 subjects demonstrate that HeadSonic achieves a commendable balanced accuracy of 96.59%, proving its efficacy and resilience against replay and synthesis attacks. Our dataset and source codes are available at https://anonymous.4open.science/r/HeadSonic-1CE4.},
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Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentication methods are all specifically designed for air-conduction earphones, which are not suited for bone conduction earphones (BCEs) that rely on bone conduction mechanisms. In this paper, we propose HeadSonic, a usable BCE authentication system based on the unique head-conducted sounds, which can be acquired when the user wears the BCE device. Specifically, the system emits a millisecond-level sound to initiate the authentication session. The signal captured by the BCE microphone is propagated through the user's head, which is unique in density, geometry, and bone-tissue ratio. It operates implicitly, while maintaining robustness across different behaviors. Extensive experiments involving 60 subjects demonstrate that HeadSonic achieves a commendable balanced accuracy of 96.59%, proving its efficacy and resilience against replay and synthesis attacks. Our dataset and source codes are available at https://anonymous.4open.science/r/HeadSonic-1CE4.
Liu, Ao; Chen, Jing; He, Kun; Du, Ruiying; Xu, Jiahua; Wu, Cong; Feng, Yebo; Li, Teng; Ma, Jianfeng
DynaShard: Secure and Adaptive Blockchain Sharding Protocol With Hybrid Consensus and Dynamic Shard Management Journal Article
In: IEEE Internet of Things Journal , vol. 12, pp. 5462-5475, 2025.
@article{nokey,
title = {DynaShard: Secure and Adaptive Blockchain Sharding Protocol With Hybrid Consensus and Dynamic Shard Management},
author = {Ao Liu and Jing Chen and Kun He and Ruiying Du and Jiahua Xu and Cong Wu and Yebo Feng and Teng Li and Jianfeng Ma},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10742075
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/DynaShard_Secure_and_Adaptive_Blockchain_Sharding_Protocol_With_Hybrid_Consensus_and_Dynamic_Shard_Management.pdf},
year = {2025},
date = {2025-03-01},
journal = {IEEE Internet of Things Journal },
volume = {12},
pages = {5462-5475},
abstract = {Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions face significant limitations in handling dynamic workloads, ensuring secure cross-shard transactions, and maintaining system integrity. To address these gaps, we propose DynaShard, a dynamic and secure cross-shard transaction processing mechanism designed to enhance blockchain sharding efficiency and security. DynaShard combines adaptive shard management, a hybrid consensus approach, plus an efficient state synchronization and dispute resolution protocol. Our performance evaluation, conducted using a robust experimental setup with real-world network conditions and transaction workloads, demonstrates DynaShard\'s superior throughput, reduced latency, and improved shard utilization compared to the fast transaction scheduling in blockchain sharding (FTSBS) method. Specifically, DynaShard achieves up to a 42.6% reduction in latency and a 78.77% improvement in shard utilization under high transaction volumes and varying cross-shard transaction ratios. These results highlight DynaShard\'s ability to outperform state-of-the-art sharding methods, ensuring scalable and resilient blockchain systems. We believe that DynaShard\'s innovative approach will significantly impact future developments in blockchain technology, paving the way for more efficient and secure distributed systems.},
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Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions face significant limitations in handling dynamic workloads, ensuring secure cross-shard transactions, and maintaining system integrity. To address these gaps, we propose DynaShard, a dynamic and secure cross-shard transaction processing mechanism designed to enhance blockchain sharding efficiency and security. DynaShard combines adaptive shard management, a hybrid consensus approach, plus an efficient state synchronization and dispute resolution protocol. Our performance evaluation, conducted using a robust experimental setup with real-world network conditions and transaction workloads, demonstrates DynaShard's superior throughput, reduced latency, and improved shard utilization compared to the fast transaction scheduling in blockchain sharding (FTSBS) method. Specifically, DynaShard achieves up to a 42.6% reduction in latency and a 78.77% improvement in shard utilization under high transaction volumes and varying cross-shard transaction ratios. These results highlight DynaShard's ability to outperform state-of-the-art sharding methods, ensuring scalable and resilient blockchain systems. We believe that DynaShard's innovative approach will significantly impact future developments in blockchain technology, paving the way for more efficient and secure distributed systems.
Sun, Jianfei; Xu, Guowen; Li, Hongwei; Zhang, Tianwei; Wu, Cong; Yang, Xuehuan; Deng, Robert H.
Sanitizable Cross-domain Access Control with Policy-driven Dynamic Authorization Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 22, pp. 4126-4142, 2025.
@article{nokey,
title = {Sanitizable Cross-domain Access Control with Policy-driven Dynamic Authorization},
author = {Jianfei Sun and Guowen Xu and Hongwei Li and Tianwei Zhang and Cong Wu and Xuehuan Yang and Robert H. Deng},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10891832
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/Sanitizable_Cross-Domain_Access_Control_With_Policy-Driven_Dynamic_Authorization-1.pdf},
doi = {10.1109/TDSC.2025.3541819},
year = {2025},
date = {2025-02-18},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {22},
pages = {4126-4142},
abstract = {The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write stipulations, governing the data permissible for senders to transmit and recipients to acquire but also enables the dynamic sharing of a data ciphertext subset with additional recipients beyond the originally sanctioned ones. We also provide comprehensive security proofs rigorously indicating the security of the invented SCPA. Moreover, to assess the efficacy of our SCPA, we undertake thorough theoretical and experimental analyses, showcasing its feasibility and superior performance.},
keywords = {},
pubstate = {published},
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The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write stipulations, governing the data permissible for senders to transmit and recipients to acquire but also enables the dynamic sharing of a data ciphertext subset with additional recipients beyond the originally sanctioned ones. We also provide comprehensive security proofs rigorously indicating the security of the invented SCPA. Moreover, to assess the efficacy of our SCPA, we undertake thorough theoretical and experimental analyses, showcasing its feasibility and superior performance.
Liang, Ruichao; Chen, Jing; Wu, Cong; He, Kun; Wu, Yueming; Cao, Ruochen; Du, Ruiying; Zhao, Ziming; Liu, Yang
Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 20, pp. 2157-2170, 2025.
@article{nokey,
title = {Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing},
author = {Ruichao Liang and Jing Chen and Cong Wu and Kun He and Yueming Wu and Ruochen Cao and Ruiying Du and Ziming Zhao and Yang Liu
},
doi = {10.1109/TIFS.2025.3537827},
year = {2025},
date = {2025-02-03},
urldate = {2025-02-03},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {20},
pages = {2157-2170},
abstract = {Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseye achieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduce Code Targets and State Targets into fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpoint Code Targets, and propose a scalable backward analysis algorithm to specify State Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseye alleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseye demonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to 9.7× , and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.},
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Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseye achieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduce Code Targets and State Targets into fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpoint Code Targets, and propose a scalable backward analysis algorithm to specify State Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseye alleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseye demonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to 9.7× , and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.
Yang, Yang; Chen, Yanjiao; Xiong, Ping; Chen, Fei; Chen, Jing
Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession Journal Article
In: IEEE Transactions on Dependable and Secure Computing, pp. 1-15, 2025.
@article{10887080,
title = {Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession},
author = {Yang Yang and Yanjiao Chen and Ping Xiong and Fei Chen and Jing Chen},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2025/02/Decentralized_Self-Auditing_Multiple_Cloud_Storage_in_Compressed_Provable_Data_Possession.pdf},
doi = {10.1109/TDSC.2025.3542068},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
pages = {1-15},
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pubstate = {published},
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2024
Liu, Ao; Chen, Jing; Du, Ruiying; Wu, Cong; Feng, Yebo; Li, Teng; Ma, Jianfeng
HeteroSample: Meta-Path Guided Sampling for Heterogeneous Graph Representation Learning Journal Article
In: IEEE Internet of Things Journal, vol. 12, pp. 4390-4402, 2024.
@article{nokey,
title = {HeteroSample: Meta-Path Guided Sampling for Heterogeneous Graph Representation Learning},
author = {Ao Liu and Jing Chen and Ruiying Du and Cong Wu and Yebo Feng and Teng Li and Jianfeng Ma},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10729862
https://datasec.whu.edu.cn/wp-content/uploads/2025/09/HeteroSample_Meta-Path_Guided_Sampling_for_Heterogeneous_Graph_Representation_Learning.pdf},
doi = {10.1109/JIOT.2024.3484996},
year = {2024},
date = {2024-10-23},
journal = {IEEE Internet of Things Journal},
volume = {12},
pages = {4390-4402},
abstract = {The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios, such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity of IoT-generated data present significant challenges, and existing methods often struggle with preserving the structural integrity and semantic richness of these complex graphs. Many current approaches fail to maintain the balance between computational efficiency and the quality of the insights generated, leading to potential loss of critical information necessary for accurate decision-making in IoT applications. We introduce HeteroSample, a novel sampling method designed to address these challenges by preserving the structural integrity, node and edge type distributions, and semantic patterns of IoT-related graphs. HeteroSample works by incorporating the novel top-leader selection, balanced neighborhood expansion, and meta-path guided sampling strategies. The key idea is to leverage the inherent heterogeneous structure and semantic relationships encoded by meta-paths to guide the sampling process. This approach ensures that the resulting subgraphs are representative of the original data while significantly reducing computational overhead. Extensive experiments demonstrate that HeteroSample outperforms state-of-the-art methods, achieving up to 15% higher F1 scores in tasks, such as link prediction and node classification, while reducing runtime by 20%. These advantages make HeteroSample a transformative tool for scalable and accurate IoT applications, enabling more effective and efficient analysis of complex IoT systems, ultimately driving advancements in smart cities, industrial IoT, and beyond.},
keywords = {},
pubstate = {published},
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}
The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios, such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity of IoT-generated data present significant challenges, and existing methods often struggle with preserving the structural integrity and semantic richness of these complex graphs. Many current approaches fail to maintain the balance between computational efficiency and the quality of the insights generated, leading to potential loss of critical information necessary for accurate decision-making in IoT applications. We introduce HeteroSample, a novel sampling method designed to address these challenges by preserving the structural integrity, node and edge type distributions, and semantic patterns of IoT-related graphs. HeteroSample works by incorporating the novel top-leader selection, balanced neighborhood expansion, and meta-path guided sampling strategies. The key idea is to leverage the inherent heterogeneous structure and semantic relationships encoded by meta-paths to guide the sampling process. This approach ensures that the resulting subgraphs are representative of the original data while significantly reducing computational overhead. Extensive experiments demonstrate that HeteroSample outperforms state-of-the-art methods, achieving up to 15% higher F1 scores in tasks, such as link prediction and node classification, while reducing runtime by 20%. These advantages make HeteroSample a transformative tool for scalable and accurate IoT applications, enabling more effective and efficient analysis of complex IoT systems, ultimately driving advancements in smart cities, industrial IoT, and beyond.
Li, Qiao; Chen, Jing; He, Kun; Zhang, Zijun; Du, Ruiying; She, Jisi; Wang, Xinxin
Model-agnostic adversarial example detection via high-frequency amplification Journal Article
In: Computers & Security, vol. 141, pp. 103791, 2024, ISSN: 0167-4048.
@article{LI2024103791,
title = {Model-agnostic adversarial example detection via high-frequency amplification},
author = {Qiao Li and Jing Chen and Kun He and Zijun Zhang and Ruiying Du and Jisi She and Xinxin Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0167404824000920
https://datasec.whu.edu.cn/wp-content/uploads/2024/04/model-agnostic-adversarial-example-detection-via-high-frequency-amplification.pdf
},
doi = {https://doi.org/10.1016/j.cose.2024.103791},
issn = {0167-4048},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers \& Security},
volume = {141},
pages = {103791},
abstract = {Image classification based on Deep Neural Networks (DNNs) is vulnerable to adversarial examples, which make the classifier output incorrect predictions. One approach to defending against this attack is to detect whether the input is an adversarial example. Unfortunately, existing adversarial example detection methods heavily rely on the underlying classifier and may fail when the classifier is upgraded. In this paper, we propose a model-agnostic detection method that leverages high-frequency signals from adversarial noises in adversarial examples and does not need interactions with the underlying classifier. We amplify redundant high-frequency signals brought by adversarial noises and represent object boundaries with these signals in an image. Our key insight is that the boundaries extracted by redundant high-frequency signals have a strong correlation with the boundaries of images in adversarial examples, while this correlation does not exist in clean images. Furthermore, adversarial examples of large images have more high-frequency signals and make adversarial detection easier on large image datasets. Experimental results show that our method has good transferability and can accurately detect various adversarial examples on different datasets.},
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Image classification based on Deep Neural Networks (DNNs) is vulnerable to adversarial examples, which make the classifier output incorrect predictions. One approach to defending against this attack is to detect whether the input is an adversarial example. Unfortunately, existing adversarial example detection methods heavily rely on the underlying classifier and may fail when the classifier is upgraded. In this paper, we propose a model-agnostic detection method that leverages high-frequency signals from adversarial noises in adversarial examples and does not need interactions with the underlying classifier. We amplify redundant high-frequency signals brought by adversarial noises and represent object boundaries with these signals in an image. Our key insight is that the boundaries extracted by redundant high-frequency signals have a strong correlation with the boundaries of images in adversarial examples, while this correlation does not exist in clean images. Furthermore, adversarial examples of large images have more high-frequency signals and make adversarial detection easier on large image datasets. Experimental results show that our method has good transferability and can accurately detect various adversarial examples on different datasets.
Jia, Meng; Chen, Jing; He, Kun; Shi, Min; Wang, Yuanzheng; Du, Ruiying
Generic Construction of Threshold Credential Management With User-Autonomy Aggregation Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 19, pp. 2549-2564, 2024, ISSN: 1556-6021.
@article{10375517,
title = {Generic Construction of Threshold Credential Management With User-Autonomy Aggregation},
author = {Meng Jia and Jing Chen and Kun He and Min Shi and Yuanzheng Wang and Ruiying Du},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10375517
https://datasec.whu.edu.cn/wp-content/uploads/2024/01/Generic_Construction_of_Threshold_Credential_Management_With_User-Autonomy_Aggregation.pdf},
doi = {10.1109/TIFS.2023.3347897},
issn = {1556-6021},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {19},
pages = {2549-2564},
abstract = {Credential management is widely used in online services such as electronic identity cards, e-health, and e-voting, in which users prove their identity or attributes with credentials issued by authorities. Under some circumstances, a user needs to prove her/his identity or attributes in multiple credentials to a verifier. In existing credential management systems, a user either proves her/his credentials one by one or requests new credentials from authorities with the original ones, and they are inefficient in practice. Moreover, existing decentralized credential management systems either rely on multiple single parties or do not support attribute revocation. In this paper, we present a threshold credential management system with threshold issuance and revocation and user-autonomy aggregation. Specifically, we design a decentralized credential management architecture where multiple authorities form an alliance and manage credentials collaboratively. Then, we propose a threshold credential management scheme, where user issuance and revocation must be approved by multiple credential managers, and a user can aggregate her/his credentials and prove them to a verifier simultaneously. We conduct experiments on our system and the results demonstrate that it is suitable in practice.},
keywords = {},
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}
Credential management is widely used in online services such as electronic identity cards, e-health, and e-voting, in which users prove their identity or attributes with credentials issued by authorities. Under some circumstances, a user needs to prove her/his identity or attributes in multiple credentials to a verifier. In existing credential management systems, a user either proves her/his credentials one by one or requests new credentials from authorities with the original ones, and they are inefficient in practice. Moreover, existing decentralized credential management systems either rely on multiple single parties or do not support attribute revocation. In this paper, we present a threshold credential management system with threshold issuance and revocation and user-autonomy aggregation. Specifically, we design a decentralized credential management architecture where multiple authorities form an alliance and manage credentials collaboratively. Then, we propose a threshold credential management scheme, where user issuance and revocation must be approved by multiple credential managers, and a user can aggregate her/his credentials and prove them to a verifier simultaneously. We conduct experiments on our system and the results demonstrate that it is suitable in practice.
陈晶,; 杨浩,; 何琨,; 李凯,; 加梦,; 杜瑞颖,
区块链扩展技术现状与展望 Journal Article
In: 软件学报, vol. 35, no. 2, pp. 828, 2024.
@article{2024828,
title = {区块链扩展技术现状与展望},
author = {陈晶 and 杨浩 and 何琨 and 李凯 and 加梦 and 杜瑞颖},
doi = {10.13328/j.cnki.jos.006954},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {软件学报},
volume = {35},
number = {2},
pages = {828},
publisher = {科学出版社},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liang, Ruichao; Chen, Jing; He, Kun; Wu, Yueming; Deng, Gelei; Du, Ruiying; Wu, Cong
PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG) Proceedings Article
In: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, Association for Computing Machinery, <conf-loc>, <city>Lisbon</city>, <country>Portugal</country>, </conf-loc>, 2024, ISBN: 9798400702174.
@inproceedings{10.1145/3597503.3623318,
title = {PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG)},
author = {Ruichao Liang and Jing Chen and Kun He and Yueming Wu and Gelei Deng and Ruiying Du and Cong Wu},
url = {https://doi.org/10.1145/3597503.3623318
https://datasec.whu.edu.cn/wp-content/uploads/2024/02/PonziGuard-DetectingPonziSchemesonEthereumwith-ContractRuntimeBehaviorGraphCRBG.pdf},
doi = {10.1145/3597503.3623318},
isbn = {9798400702174},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the 46th IEEE/ACM International Conference on Software Engineering},
publisher = {Association for Computing Machinery},
address = {\<conf-loc\>, \<city\>Lisbon\</city\>, \<country\>Portugal\</country\>, \</conf-loc\>},
series = {ICSE '24},
abstract = {Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.In this paper, we propose PonziGuard, an efficient Ponzi scheme detection approach based on contract runtime behavior. Inspired by the observation that a contract's runtime behavior is more effective in disguising Ponzi contracts from the innocent contracts, PonziGuard establishes a comprehensive graph representation called contract runtime behavior graph (CRBG), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task, enhancing its overall effectiveness. We conducted comparative experiments on a ground-truth dataset and applied PonziGuard to Ethereum Mainnet. The results show that PonziGuard outperforms the current state-of-the-art approaches and is also effective in open environments. Using PonziGuard, we have identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately $500 million USD.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.In this paper, we propose PonziGuard, an efficient Ponzi scheme detection approach based on contract runtime behavior. Inspired by the observation that a contract's runtime behavior is more effective in disguising Ponzi contracts from the innocent contracts, PonziGuard establishes a comprehensive graph representation called contract runtime behavior graph (CRBG), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task, enhancing its overall effectiveness. We conducted comparative experiments on a ground-truth dataset and applied PonziGuard to Ethereum Mainnet. The results show that PonziGuard outperforms the current state-of-the-art approaches and is also effective in open environments. Using PonziGuard, we have identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately $500 million USD.
Wang, Mei; Chen, Jing; He, Kun; Yu, Ruozhou; Du, Ruiying; Qian, Zhihao
UFinAKA: Fingerprint-Based Authentication and Key Agreement With Updatable Blind Credentials Journal Article
In: IEEE/ACM Transactions on Networking, vol. 32, iss. 2, pp. 1110-1123, 2024.
@article{10250445,
title = {UFinAKA: Fingerprint-Based Authentication and Key Agreement With Updatable Blind Credentials},
author = {Mei Wang and Jing Chen and Kun He and Ruozhou Yu and Ruiying Du and Zhihao Qian},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/03/UFinAKA-Fingerprint-Based-Authentication-and-Key-Agreement-With-Updatable-Bind-Credentials.pdf},
doi = {10.1109/TNET.2023.3311130},
year = {2024},
date = {2024-01-01},
urldate = {2023-01-01},
journal = {IEEE/ACM Transactions on Networking},
volume = {32},
issue = {2},
pages = {1110-1123},
keywords = {},
pubstate = {published},
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何琨,; 李瀚星,; 陈晶,
基于分层结构的匹配量隐藏加密多重映射方案 Journal Article
In: 通信学报, vol. 45, no. 1, pp. 94, 2024.
@article{陈晶:94,
title = {基于分层结构的匹配量隐藏加密多重映射方案},
author = {何琨 and 李瀚星 and 陈晶},
url = {https://www.infocomm-journal.com/txxb/CN/abstract/article_174131.shtml
https://datasec.whu.edu.cn/wp-content/uploads/2024/04/基于分层结构的匹配量隐藏加密多重映射方案.pdf},
doi = {10.11959/j.issn.1000-436x.2024002},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {通信学报},
volume = {45},
number = {1},
pages = {94},
publisher = {通信学报},
keywords = {},
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tppubtype = {article}
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李瞧,; 陈晶,; 张子君,; 何琨,; 杜瑞颖,; 汪欣欣,
基于随机平滑的通用黑盒认证防御 Journal Article
In: 计算机学报, vol. 47, no. 03, pp. 690-702, 2024, ISSN: 0254-4164.
@article{JSJX202403011,
title = {基于随机平滑的通用黑盒认证防御},
author = {李瞧 and 陈晶 and 张子君 and 何琨 and 杜瑞颖 and 汪欣欣},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/04/基于随机平滑的通用黑盒认证防御_李瞧.pdf},
issn = {0254-4164},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {计算机学报},
volume = {47},
number = {03},
pages = {690-702},
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何琨,; 佘计思,; 张子君,; 陈晶,; 汪欣欣,; 杜瑞颖,
基于引导扩散模型的自然对抗补丁生成方法 Journal Article
In: 电子学报, vol. 52, no. 2, pp. 564-573, 2024.
@article{何琨:564,
title = {基于引导扩散模型的自然对抗补丁生成方法},
author = {何琨 and 佘计思 and 张子君 and 陈晶 and 汪欣欣 and 杜瑞颖},
url = {https://www.ejournal.org.cn/CN/10.12263/DZXB.20230481
https://datasec.whu.edu.cn/wp-content/uploads/2024/06/基于引导扩散模型的自然对抗补丁生成方法.pdf},
doi = {10.12263/DZXB.20230481},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {电子学报},
volume = {52},
number = {2},
pages = {564-573},
publisher = {电子学报},
abstract = {\<p\>近年来，物理世界中的对抗补丁攻击因其对深度学习模型安全的影响而引起了广泛关注.现有的工作主要集中在生成在物理世界中攻击性能良好的对抗补丁，没有考虑到对抗补丁图案与自然图像的差别，因此生成的对抗补丁往往不自然且容易被观察者发现.为了解决这个问题，本文提出了一种基于引导的扩散模型的自然对抗补丁生成方法.具体而言，本文通过解析目标检测器的输出构建预测对抗补丁攻击成功率的预测器，利用该预测器的梯度作为条件引导预训练的扩散模型的逆扩散过程，从而生成自然度更高且保持高攻击成功率的对抗补丁.本文在数字世界和物理世界中进行了广泛的实验，评估了对抗补丁针对各种目标检测模型的攻击效果以及对抗补丁的自然度.实验结果表明，通过将所构建的攻击成功率预测器与扩散模型相结合，本文的方法能够生成比现有方案更自然的对抗补丁，同时保持攻击性能.\</p\>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<p>近年来，物理世界中的对抗补丁攻击因其对深度学习模型安全的影响而引起了广泛关注.现有的工作主要集中在生成在物理世界中攻击性能良好的对抗补丁，没有考虑到对抗补丁图案与自然图像的差别，因此生成的对抗补丁往往不自然且容易被观察者发现.为了解决这个问题，本文提出了一种基于引导的扩散模型的自然对抗补丁生成方法.具体而言，本文通过解析目标检测器的输出构建预测对抗补丁攻击成功率的预测器，利用该预测器的梯度作为条件引导预训练的扩散模型的逆扩散过程，从而生成自然度更高且保持高攻击成功率的对抗补丁.本文在数字世界和物理世界中进行了广泛的实验，评估了对抗补丁针对各种目标检测模型的攻击效果以及对抗补丁的自然度.实验结果表明，通过将所构建的攻击成功率预测器与扩散模型相结合，本文的方法能够生成比现有方案更自然的对抗补丁，同时保持攻击性能.</p>
Gu, Yangyang; Chen, Jing; He, Kun; Wu, Cong; Zhao, Ziming; Du, Ruiying
WiFiLeaks: Exposing Stationary Human Presence Through a Wall With Commodity Mobile Devices Journal Article
In: IEEE Transactions on Mobile Computing, vol. 23, no. 6, pp. 6997-7011, 2024.
@article{10301514,
title = {WiFiLeaks: Exposing Stationary Human Presence Through a Wall With Commodity Mobile Devices},
author = {Yangyang Gu and Jing Chen and Kun He and Cong Wu and Ziming Zhao and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/06/WiFiLeaks_Exposing_Stationary_Human_Presence_Through_a_Wall_With_Commodity_Mobile_Devices.pdf},
doi = {10.1109/TMC.2023.3328349},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {23},
number = {6},
pages = {6997-7011},
abstract = {WiFi devices are ubiquitous and may leak user and household privacy. In this paper, we report an attack, namely WiFiLeaks, which uses a commodity mobile device to passively detect stationary human presence through a wall by analyzing the channel state information of wireless signals transmitted by indoor WiFi devices. In our adversarial scenario, attackers cannot control the WiFi transmitter or use advanced radio devices. The main challenge of this attack is how to extract robust features from non-customized signals for stationary human presence. To address this challenge, we first combine methods based on outliers and wavelet denoising to enhance the low-frequency information related to human presence. Then we propose a novel feature extraction method based on the correlation among subcarriers since stationary human presence can enhance their correlations. We evaluate WiFiLeaks using nine different WiFi transmitter and one commodity smartphone in four different settings. The evaluations show WiFiLeaks can still achieve accuracy rates of 83.33% and 100% for human presence and absence at 20 meters between the monitor device and the transmitter in through-the-wall scenarios.},
keywords = {},
pubstate = {published},
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}
WiFi devices are ubiquitous and may leak user and household privacy. In this paper, we report an attack, namely WiFiLeaks, which uses a commodity mobile device to passively detect stationary human presence through a wall by analyzing the channel state information of wireless signals transmitted by indoor WiFi devices. In our adversarial scenario, attackers cannot control the WiFi transmitter or use advanced radio devices. The main challenge of this attack is how to extract robust features from non-customized signals for stationary human presence. To address this challenge, we first combine methods based on outliers and wavelet denoising to enhance the low-frequency information related to human presence. Then we propose a novel feature extraction method based on the correlation among subcarriers since stationary human presence can enhance their correlations. We evaluate WiFiLeaks using nine different WiFi transmitter and one commodity smartphone in four different settings. The evaluations show WiFiLeaks can still achieve accuracy rates of 83.33% and 100% for human presence and absence at 20 meters between the monitor device and the transmitter in through-the-wall scenarios.
Zhang, Renli; Zhou, Ruiting; Wang, Yufeng; Tan, Haisheng; He, Kun
Incentive Mechanisms for Online Task Offloading With Privacy-Preserving in UAV-Assisted Mobile Edge Computing Journal Article
In: IEEE/ACM Transactions on Networking, vol. 32, no. 3, pp. 2646-2661, 2024.
@article{10440643,
title = {Incentive Mechanisms for Online Task Offloading With Privacy-Preserving in UAV-Assisted Mobile Edge Computing},
author = {Renli Zhang and Ruiting Zhou and Yufeng Wang and Haisheng Tan and Kun He},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/06/Incentive_Mechanisms_for_Online_Task_Offloading_With_Privacy-Preserving_in_UAV-Assisted_Mobile_Edge_Computing_compressed.pdf},
doi = {10.1109/TNET.2024.3364141},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE/ACM Transactions on Networking},
volume = {32},
number = {3},
pages = {2646-2661},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Yan, Ran; Du, Ruiying; He, Kun; Chen, Jing
Efficient Adversarial Training with Membership Inference Resistance Proceedings Article
In: Liu, Qingshan; Wang, Hanzi; Ma, Zhanyu; Zheng, Weishi; Zha, Hongbin; Chen, Xilin; Wang, Liang; Ji, Rongrong (Ed.): Pattern Recognition and Computer Vision, pp. 474–486, Springer Nature Singapore, Singapore, 2023, ISBN: 978-981-99-8429-9.
@inproceedings{10.1007/978-981-99-8429-9_38,
title = {Efficient Adversarial Training with Membership Inference Resistance},
author = {Ran Yan and Ruiying Du and Kun He and Jing Chen},
editor = {Qingshan Liu and Hanzi Wang and Zhanyu Ma and Weishi Zheng and Hongbin Zha and Xilin Chen and Liang Wang and Rongrong Ji},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/01/978-981-99-8429-9_38.pdf},
isbn = {978-981-99-8429-9},
year = {2023},
date = {2023-12-30},
urldate = {2024-01-01},
booktitle = {Pattern Recognition and Computer Vision},
pages = {474\textendash486},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep cross-media computing faces adversarial example attacks, adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this paper, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example, called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement the framework based on three adversarial training methods on FMNIST and CIFAR10. The experimental results show that our framework outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep cross-media computing faces adversarial example attacks, adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this paper, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example, called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement the framework based on three adversarial training methods on FMNIST and CIFAR10. The experimental results show that our framework outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime.
Shi, Min; Chen, Jing; He, Kun; Zhao, Haoran; Jia, Meng; Du, Ruiying
Formal Analysis and Patching of BLE-SC Pairing Proceedings Article
In: 32nd USENIX Security Symposium (USENIX Security 23), pp. 37–52, USENIX Association, Anaheim, CA, 2023, ISBN: 978-1-939133-37-3.
@inproceedings{287101,
title = {Formal Analysis and Patching of BLE-SC Pairing},
author = {Min Shi and Jing Chen and Kun He and Haoran Zhao and Meng Jia and Ruiying Du},
url = {https://www.usenix.org/conference/usenixsecurity23/presentation/shi-min
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/usenixsecurity23-shi-min.pdf
},
isbn = {978-1-939133-37-3},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
pages = {37\textendash52},
publisher = {USENIX Association},
address = {Anaheim, CA},
abstract = {Bluetooth Low Energy (BLE) is the mainstream Bluetooth standard and BLE Secure Connections (BLC-SC) pairing is a protocol that authenticates two Bluetooth devices and derives a shared secret key between them. Although BLE-SC pairing employs well-studied cryptographic primitives to guarantee its security, a recent study revealed a logic flaw in the protocol.
In this paper, we develop the first comprehensive formal model of the BLE-SC pairing protocol. Our model is compliant with the latest Bluetooth specification version 5.3 and covers all association models in the specification to discover attacks caused by the interplay between different association models. We also partly loosen the perfect cryptography assumption in traditional symbolic analysis approaches by designing a low-entropy key oracle to detect attacks caused by the poorly derived keys. Our analysis confirms two existing attacks and discloses a new attack. We propose a countermeasure to fix the flaws found in the BLE-SC pairing protocol and discuss the backward compatibility. Moreover, we extend our model to verify the countermeasure, and the results demonstrate its effectiveness in our extended model.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bluetooth Low Energy (BLE) is the mainstream Bluetooth standard and BLE Secure Connections (BLC-SC) pairing is a protocol that authenticates two Bluetooth devices and derives a shared secret key between them. Although BLE-SC pairing employs well-studied cryptographic primitives to guarantee its security, a recent study revealed a logic flaw in the protocol.
In this paper, we develop the first comprehensive formal model of the BLE-SC pairing protocol. Our model is compliant with the latest Bluetooth specification version 5.3 and covers all association models in the specification to discover attacks caused by the interplay between different association models. We also partly loosen the perfect cryptography assumption in traditional symbolic analysis approaches by designing a low-entropy key oracle to detect attacks caused by the poorly derived keys. Our analysis confirms two existing attacks and discloses a new attack. We propose a countermeasure to fix the flaws found in the BLE-SC pairing protocol and discuss the backward compatibility. Moreover, we extend our model to verify the countermeasure, and the results demonstrate its effectiveness in our extended model.
In this paper, we develop the first comprehensive formal model of the BLE-SC pairing protocol. Our model is compliant with the latest Bluetooth specification version 5.3 and covers all association models in the specification to discover attacks caused by the interplay between different association models. We also partly loosen the perfect cryptography assumption in traditional symbolic analysis approaches by designing a low-entropy key oracle to detect attacks caused by the poorly derived keys. Our analysis confirms two existing attacks and discloses a new attack. We propose a countermeasure to fix the flaws found in the BLE-SC pairing protocol and discuss the backward compatibility. Moreover, we extend our model to verify the countermeasure, and the results demonstrate its effectiveness in our extended model.
汪欣欣,; 陈晶,; 何琨,; 张子君,; 杜瑞颖,; 李瞧,; 佘计思,
面向目标检测的对抗攻击与防御综述 Journal Article
In: 通信学报, vol. 44, no. 11, pp. 260, 2023.
@article{汪欣欣:260,
title = {面向目标检测的对抗攻击与防御综述},
author = {汪欣欣 and 陈晶 and 何琨 and 张子君 and 杜瑞颖 and 李瞧 and 佘计思 },
url = {https://www.infocomm-journal.com/txxb/CN/abstract/article_173990.shtml},
doi = {10.11959/j.issn.1000-436x.2023223},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {通信学报},
volume = {44},
number = {11},
pages = {260},
publisher = {通信学报},
keywords = {},
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}
Gu, Yangyang; Chen, Jing; Wu, Cong; He, Kun; Zhao, Ziming; Du, Ruiying
LocCams: An Efficient and Robust Approach for Detecting and Localizing Hidden Wireless Cameras via Commodity Devices Journal Article
In: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 7, no. 4, 2023.
@article{10.1145/3631432,
title = {LocCams: An Efficient and Robust Approach for Detecting and Localizing Hidden Wireless Cameras via Commodity Devices},
author = {Yangyang Gu and Jing Chen and Cong Wu and Kun He and Ziming Zhao and Ruiying Du},
url = {https://doi.org/10.1145/3631432
https://datasec.whu.edu.cn/wp-content/uploads/2024/01/LocCams-AnEfficientandRobustApproachforDetectingand-LocalizingHiddenWirelessCamerasviaCommodityDevices.pdf},
doi = {10.1145/3631432},
year = {2023},
date = {2023-01-01},
urldate = {2024-01-01},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
volume = {7},
number = {4},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.
Yang, Xuanang; Chen, Jing; He, Kun; Bai, Hao; Wu, Cong; Du, Ruiying
Efficient Privacy-Preserving Inference Outsourcing for Convolutional Neural Networks Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4815-4829, 2023, ISSN: 1556-6021.
@article{10154059,
title = {Efficient Privacy-Preserving Inference Outsourcing for Convolutional Neural Networks},
author = {Xuanang Yang and Jing Chen and Kun He and Hao Bai and Cong Wu and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Efficient_Privacy-preserving_Inference_Outsourcing_for_Convolutional_Neural_Networks.pdf},
doi = {10.1109/TIFS.2023.3287072},
issn = {1556-6021},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {18},
pages = {4815-4829},
abstract = {Inference outsourcing enables model owners to deploy their machine learning models on cloud servers to serve users. In this paradigm, the privacy of model owners and users should be considered. Existing solutions focus on Convolutional Neural Networks (CNNs) but their efficiency is much lower than GALA, which is a solution that only protects user privacy. Furthermore, these solutions adopt approximations that reduce the model accuracy and thus require model owners to retrain the models. In this paper, we present an efficient CNN inference outsourcing solution that protects the privacy of both model owners and users. Specifically, we design secure two-party computation protocols based on two non-colluding cloud servers, which calculate with additive secret shares of the model and the user’s input. Our protocols avoid the expensive permutation operations in linear calculations and approximations in non-linear calculations. We implement our solution on realistic CNNs and experimental results show that our solution is even 2\textendash4 times faster than GALA.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Inference outsourcing enables model owners to deploy their machine learning models on cloud servers to serve users. In this paradigm, the privacy of model owners and users should be considered. Existing solutions focus on Convolutional Neural Networks (CNNs) but their efficiency is much lower than GALA, which is a solution that only protects user privacy. Furthermore, these solutions adopt approximations that reduce the model accuracy and thus require model owners to retrain the models. In this paper, we present an efficient CNN inference outsourcing solution that protects the privacy of both model owners and users. Specifically, we design secure two-party computation protocols based on two non-colluding cloud servers, which calculate with additive secret shares of the model and the user’s input. Our protocols avoid the expensive permutation operations in linear calculations and approximations in non-linear calculations. We implement our solution on realistic CNNs and experimental results show that our solution is even 2–4 times faster than GALA.
2022
Chen, Jing; Chen, Xin; He, Kun; Du, Ruiying; Chen, Weihang; Xiang, Yang
DELIA: Distributed Efficient Log Integrity Audit Based on Hierarchal Multi-Party State Channel Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3286–3300, 2022, ISSN: 1941-0018.
@article{CCH+22,
title = {DELIA: Distributed Efficient Log Integrity Audit Based on Hierarchal Multi-Party State Channel},
author = {Jing Chen and Xin Chen and Kun He and Ruiying Du and Weihang Chen and Yang Xiang},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/DELIA_Distributed_Efficient_Log_Integrity_Audit_Based_on_Hierarchal_Multi-Party_State_Channel.pdf},
doi = {10.1109/TDSC.2021.3092365},
issn = {1941-0018},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {19},
number = {5},
pages = {3286--3300},
abstract = {Audit log contains the trace of different activities in computing systems, which makes it critical for security management, censorship, and forensics. However, experienced attackers may delete or modify the audit log after their attacks, which makes the audit log unavailable in attack investigation. In this article, we focus on the log integrity audit in the same domain, in which a number of servers update audit logs for a single or several organizations as an alliance. We propose a distributed efficient log integrity audit framework, called DELIA, which employs the distributed ledger technique to protect audit information, and utilizes the idea of state channel to improve the throughput of distributed ledger. To generate stable state from the rapidly-updated logs in the domain, we propose a log state generation scheme, which not only generates state suitable for audit logs, but also enables mutual supervision within the domain. To overcome the high latency in existing state channel schemes, we propose a hierarchal multi-party state channel scheme, which makes the latency in our framework independent of the number of servers in the domain. We implement DELIA on Ethereum and evaluate its performance. The results show that our framework is efficient and secure in practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Audit log contains the trace of different activities in computing systems, which makes it critical for security management, censorship, and forensics. However, experienced attackers may delete or modify the audit log after their attacks, which makes the audit log unavailable in attack investigation. In this article, we focus on the log integrity audit in the same domain, in which a number of servers update audit logs for a single or several organizations as an alliance. We propose a distributed efficient log integrity audit framework, called DELIA, which employs the distributed ledger technique to protect audit information, and utilizes the idea of state channel to improve the throughput of distributed ledger. To generate stable state from the rapidly-updated logs in the domain, we propose a log state generation scheme, which not only generates state suitable for audit logs, but also enables mutual supervision within the domain. To overcome the high latency in existing state channel schemes, we propose a hierarchal multi-party state channel scheme, which makes the latency in our framework independent of the number of servers in the domain. We implement DELIA on Ethereum and evaluate its performance. The results show that our framework is efficient and secure in practice.
Chen, Jing; Zhan, Zeyi; He, Kun; Du, Ruiying; Wang, Donghui; Liu, Fei
XAuth: Efficient Privacy-Preserving Cross-Domain Authentication Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3301–3311, 2022, ISSN: 1941-0018.
@article{CZH+22,
title = {XAuth: Efficient Privacy-Preserving Cross-Domain Authentication},
author = {Jing Chen and Zeyi Zhan and Kun He and Ruiying Du and Donghui Wang and Fei Liu},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/XAuth_Efficient_Privacy-Preserving_Cross-Domain_Authentication.pdf},
doi = {10.1109/TDSC.2021.3092375},
issn = {1941-0018},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {19},
number = {5},
pages = {3301--3311},
abstract = {It is well known that each Public Key Infrastructure (PKI) system forms a closed security domain and only recognizes certificates in its own domain (such as medical systems, financial systems, and 5G networks). When users need to access services in other domains, their identities often cannot be recognized or PKI systems require extremely complex operations to authenticate the users’ identities. This is the cross-domain authentication problem. The distributed consensus feature of blockchain provides a technical approach to solve this problem. However, there are some unresolved problems in existing blockchain-based schemes. On one hand, due to the low throughput of blockchain systems, the response speed may be insufferable when the number of cross-domain authentication requirements becomes enormous. On the other hand, these schemes insufficiently consider the privacy risk in the cross-domain scenario. In this article, we propose an efficient privacy-preserving cross-domain authentication scheme called XAuth that is integrated naturally with the existing PKI and Certificate Transparency (CT) systems. Specifically, we design a lightweight correctness verification protocol based on Multiple Merkle Hash Tree for rapid response. To protect users’ privacy, we present an anonymous authentication protocol for cross-domain authentication. The security analysis and experimental results demonstrate that XAuth is secure and efficient.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
It is well known that each Public Key Infrastructure (PKI) system forms a closed security domain and only recognizes certificates in its own domain (such as medical systems, financial systems, and 5G networks). When users need to access services in other domains, their identities often cannot be recognized or PKI systems require extremely complex operations to authenticate the users’ identities. This is the cross-domain authentication problem. The distributed consensus feature of blockchain provides a technical approach to solve this problem. However, there are some unresolved problems in existing blockchain-based schemes. On one hand, due to the low throughput of blockchain systems, the response speed may be insufferable when the number of cross-domain authentication requirements becomes enormous. On the other hand, these schemes insufficiently consider the privacy risk in the cross-domain scenario. In this article, we propose an efficient privacy-preserving cross-domain authentication scheme called XAuth that is integrated naturally with the existing PKI and Certificate Transparency (CT) systems. Specifically, we design a lightweight correctness verification protocol based on Multiple Merkle Hash Tree for rapid response. To protect users’ privacy, we present an anonymous authentication protocol for cross-domain authentication. The security analysis and experimental results demonstrate that XAuth is secure and efficient.
Zhou, Ruiting; Zhang, Renli; Wang, Yufeng; Tan, Haisheng; He, Kun
Online incentive mechanism for task offloading with privacy-preserving in UAV-assisted mobile edge computing Proceedings Article
In: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pp. 211–220, 2022, ISBN: 9781450391658.
@inproceedings{ZZW+22,
title = {Online incentive mechanism for task offloading with privacy-preserving in UAV-assisted mobile edge computing},
author = {Ruiting Zhou and Renli Zhang and Yufeng Wang and Haisheng Tan and Kun He},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/Online_incentive_mechanism_for_task_offloading_with_privacy-preserving_in_UAV-assisted_mobile_edge_computing-.pdf},
doi = {10.1145/3492866.3549715},
isbn = {9781450391658},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},
pages = {211--220},
abstract = {Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs, i.e., high mobility as well as limited energy and computing capacity, and propose a privacy-preserving auction framework, Ptero, to schedule offloading tasks on the fly and incentivize UAVs' participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV's energy constraint into the objective. To protect UE's privacy, Ptero calculates UAV's coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system's utility (i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy preserving and a non-trivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to 116% compared with four state-of-the-art algorithms.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs, i.e., high mobility as well as limited energy and computing capacity, and propose a privacy-preserving auction framework, Ptero, to schedule offloading tasks on the fly and incentivize UAVs' participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV's energy constraint into the objective. To protect UE's privacy, Ptero calculates UAV's coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system's utility (i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy preserving and a non-trivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to 116% compared with four state-of-the-art algorithms.
Wu, Cong; Chen, Jing; He, Kun; Zhao, Ziming; Du, Ruiying; Zhang, Chen
EchoHand: High Accuracy and Presentation Attack Resistant Hand Authentication on Commodity Mobile Devices Proceedings Article
In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 2931–2945, 2022, ISBN: 9781450394505.
@inproceedings{WCH+22,
title = {EchoHand: High Accuracy and Presentation Attack Resistant Hand Authentication on Commodity Mobile Devices},
author = {Cong Wu and Jing Chen and Kun He and Ziming Zhao and Ruiying Du and Chen Zhang},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/EchoHand-High-Accuracy-and-Presentation-Attack-Resistant-Hand-Authentication-on-Commodity-Mobile-Devices.pdf},
doi = {10.1145/3548606.3560553},
isbn = {9781450394505},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
pages = {2931--2945},
abstract = {Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. However, existing hand authentication schemes use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with active acoustic sensing of the other holding hand. EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. Moreover, EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability and security of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. However, existing hand authentication schemes use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with active acoustic sensing of the other holding hand. EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. Moreover, EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability and security of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks.
Wu, Cong; He, Kun; Chen, Jing; Zhao, Ziming; Du, Ruiying
Toward Robust Detection of Puppet Attacks via Characterizing Fingertip-Touch Behaviors Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 6, pp. 4002–4018, 2022, ISSN: 1941-0018.
@article{WHC+22a,
title = {Toward Robust Detection of Puppet Attacks via Characterizing Fingertip-Touch Behaviors},
author = {Cong Wu and Kun He and Jing Chen and Ziming Zhao and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/Toward_Robust_Detection_of_Puppet_Attacks_via_Characterizing_Fingertip-Touch_Behaviors.pdf},
doi = {10.1109/TDSC.2021.3116552},
issn = {1941-0018},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {19},
number = {6},
pages = {4002--4018},
abstract = {Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namely puppet attack, in which an attacker places an unwilling victim's finger on the fingerprint sensor. In this article, we propose FinAuth, an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks using fingertip-touch characteristics. FinAuth characterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates. FinAuth only utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness of FinAuth, we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show that FinAuth can achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate that FinAuth is resilient against possible attacks. In addition, we report the usability analysis results of FinAuth, including user authentication delay and overhead.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namely puppet attack, in which an attacker places an unwilling victim's finger on the fingerprint sensor. In this article, we propose FinAuth, an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks using fingertip-touch characteristics. FinAuth characterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates. FinAuth only utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness of FinAuth, we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show that FinAuth can achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate that FinAuth is resilient against possible attacks. In addition, we report the usability analysis results of FinAuth, including user authentication delay and overhead.
Jia, Meng; Chen, Jing; He, Kun; Du, Ruiying; Zheng, Li; Lai, Mingxi; Wang, Donghui; Liu, Fei
Redactable Blockchain from Decentralized Chameleon Hash Functions Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2771–2783, 2022, ISSN: 1556-6021.
@article{JCH+22,
title = {Redactable Blockchain from Decentralized Chameleon Hash Functions},
author = {Meng Jia and Jing Chen and Kun He and Ruiying Du and Li Zheng and Mingxi Lai and Donghui Wang and Fei Liu},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/Redactable_Blockchain_From_Decentralized_Chameleon_Hash_Functions.pdf},
doi = {10.1109/TIFS.2022.3192716},
issn = {1556-6021},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {17},
pages = {2771--2783},
abstract = {Blockchain is a technology with decentralization and immutability features and has been employed for auditing by many applications. However, immutability sometimes limits the application of blockchain technology. For example, vulnerable smart contracts on blockchain cannot be redacted due to immutability. The existing redactable blockchain solutions either have a low efficiency or violate the decentralization feature. Moreover, those solutions lack mechanisms for tracing redaction history and checking block consistency. In this paper, we present an efficient redactable blockchain with traceability in the decentralized setting. Specifically, we propose a decentralized chameleon hash function for redactable blockchain that every redaction must be approved by multiple blockchain nodes. We also design a redactable blockchain structure that maintains all redactions of a block and encodes the redacted blocks into an RSA accumulator. Then, we propose an efficient block consistency check protocol based on the RSA accumulator. Finally, we conduct experiments and compare our scheme with another decentralized redactable blockchain to demonstrate that our solution is efficient in practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Blockchain is a technology with decentralization and immutability features and has been employed for auditing by many applications. However, immutability sometimes limits the application of blockchain technology. For example, vulnerable smart contracts on blockchain cannot be redacted due to immutability. The existing redactable blockchain solutions either have a low efficiency or violate the decentralization feature. Moreover, those solutions lack mechanisms for tracing redaction history and checking block consistency. In this paper, we present an efficient redactable blockchain with traceability in the decentralized setting. Specifically, we propose a decentralized chameleon hash function for redactable blockchain that every redaction must be approved by multiple blockchain nodes. We also design a redactable blockchain structure that maintains all redactions of a block and encodes the redacted blocks into an RSA accumulator. Then, we propose an efficient block consistency check protocol based on the RSA accumulator. Finally, we conduct experiments and compare our scheme with another decentralized redactable blockchain to demonstrate that our solution is efficient in practice.
Wang, Mei; He, Kun; Chen, Jing; Du, Ruiying; Zhang, Bingsheng; Li, Zengpeng
PANDA: Lightweight non-interactive privacy-preserving data aggregation for constrained devices Journal Article
In: Future Generation Computer Systems, vol. 131, pp. 28–42, 2022, ISSN: 0167-739X.
@article{WHC+22,
title = {PANDA: Lightweight non-interactive privacy-preserving data aggregation for constrained devices},
author = {Mei Wang and Kun He and Jing Chen and Ruiying Du and Bingsheng Zhang and Zengpeng Li},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/PANDA_Lightweight_non-interactive_privacy-preserving-data_aggregation_for_constrained_devices.pdf},
doi = {10.1016/j.future.2022.01.007},
issn = {0167-739X},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Future Generation Computer Systems},
volume = {131},
pages = {28--42},
abstract = {Privacy-preserving data aggregation is becoming a demanding necessity for many promising scenarios, e.g., health care analysis. Sensitive data are collected and aggregated in a privacy-preserving approach using current Internet of Things (IoT) technology, leading to immense challenge and consequent interest in developing secure computing algorithms for individual and organizational data. However, most existing solutions focus on specific evaluations (e.g., SUM), and they use heavy cryptographic techniques, which are far from practice for constrained IoT devices. The Trusted Execution Environment (TEE, implemented with Intel SGX) can assist in computing arbitrary functions and avoiding resource-consuming operations. Nevertheless, TEE is subject to several challenges because TEE is vulnerable to limited resource and even function violations, e.g., the attacker may bypass the boundary of TEE. In this paper, we propose a lightweight non-interactive privacy-preserving data aggregation scheme for resource-constrained devices, named PANDA, where TEE is introduced to bypass the trusted entities requirement and heavy overhead. Additionally, PANDA explores the certificate-aided function authorization to prevent leakage from unauthorized functions, and designs the public verifiable certificate management to detect the abnormal behaviors of the host to mitigate the external host compromise. We formalize PANDA with rigorous security analysis to indicate privacy protection against the compromised aggregator and analyst. The evaluation results show that PANDA has constant online communication cost and lightweight computation overhead for constrained devices, which is suitable for IoT applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Privacy-preserving data aggregation is becoming a demanding necessity for many promising scenarios, e.g., health care analysis. Sensitive data are collected and aggregated in a privacy-preserving approach using current Internet of Things (IoT) technology, leading to immense challenge and consequent interest in developing secure computing algorithms for individual and organizational data. However, most existing solutions focus on specific evaluations (e.g., SUM), and they use heavy cryptographic techniques, which are far from practice for constrained IoT devices. The Trusted Execution Environment (TEE, implemented with Intel SGX) can assist in computing arbitrary functions and avoiding resource-consuming operations. Nevertheless, TEE is subject to several challenges because TEE is vulnerable to limited resource and even function violations, e.g., the attacker may bypass the boundary of TEE. In this paper, we propose a lightweight non-interactive privacy-preserving data aggregation scheme for resource-constrained devices, named PANDA, where TEE is introduced to bypass the trusted entities requirement and heavy overhead. Additionally, PANDA explores the certificate-aided function authorization to prevent leakage from unauthorized functions, and designs the public verifiable certificate management to detect the abnormal behaviors of the host to mitigate the external host compromise. We formalize PANDA with rigorous security analysis to indicate privacy protection against the compromised aggregator and analyst. The evaluation results show that PANDA has constant online communication cost and lightweight computation overhead for constrained devices, which is suitable for IoT applications.
Wang, Run; Li, Haoxuan; Mu, Lingzhou; Ren, Jixing; Guo, Shangwei; Liu, Li; Fang, Liming; Chen, Jing; Wang, Lina
Rethinking the Vulnerability of DNN Watermarking: Are Watermarks Robust against Naturalness-Aware Perturbations? Proceedings Article
In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1808–1818, Association for Computing Machinery, Lisboa, Portugal, 2022, ISBN: 9781450392037.
@inproceedings{10.1145/3503161.3548390,
title = {Rethinking the Vulnerability of DNN Watermarking: Are Watermarks Robust against Naturalness-Aware Perturbations?},
author = {Run Wang and Haoxuan Li and Lingzhou Mu and Jixing Ren and Shangwei Guo and Li Liu and Liming Fang and Jing Chen and Lina Wang},
url = {https://doi.org/10.1145/3503161.3548390
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Rethinking-the-Vulnerability-of-DNN-Watermarking-Are-Watermarks-Robust-against-Naturalness-aware-Perturbations-.pdf
},
doi = {10.1145/3503161.3548390},
isbn = {9781450392037},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {1808\textendash1818},
publisher = {Association for Computing Machinery},
address = {Lisboa, Portugal},
series = {MM '22},
abstract = {Training Deep Neural Networks (DNN) is a time-consuming process and requires a large amount of training data, which motivates studies working on protecting the intellectual property (IP) of DNN models by employing various watermarking techniques. Unfortunately, in recent years, adversaries have been exploiting the vulnerabilities of the employed watermarking techniques to remove the embedded watermarks. In this paper, we investigate and introduce a novel watermark removal attack, called AdvNP, against all the existing four different types of DNN watermarking schemes via input preprocessing by injecting \underline{Adv}ersarial \underline{N}aturalness-aware \underline{P}erturbations. In contrast to the prior studies, our proposed method is the first work that generalizes all the existing four watermarking schemes well without involving any model modification, which preserves the fidelity of the target model. We conduct the experiments against four state-of-the-art (SOTA) watermarking schemes on two real tasks (e.g., image classification on ImageNet, face recognition on CelebA) across multiple DNN models. Overall, our proposed AdvNP significantly invalidates the watermarks against the four watermarking schemes on two real-world datasets, i.e., 60.9% on the average attack success rate and up to 97% in the worse case. Moreover, our AdvNP could well survive the image denoising techniques and outperforms the baseline in both the fidelity preserving and watermark removal. Furthermore, we introduce two defense methods to enhance the robustness of DNN watermarking against our AdvNP. Our experimental results pose real threats to the existing watermarking schemes and call for more practical and robust watermarking techniques to protect the copyright of pre-trained DNN models. The source code and models are available at ttps://github.com/GitKJ123/AdvNP.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Training Deep Neural Networks (DNN) is a time-consuming process and requires a large amount of training data, which motivates studies working on protecting the intellectual property (IP) of DNN models by employing various watermarking techniques. Unfortunately, in recent years, adversaries have been exploiting the vulnerabilities of the employed watermarking techniques to remove the embedded watermarks. In this paper, we investigate and introduce a novel watermark removal attack, called AdvNP, against all the existing four different types of DNN watermarking schemes via input preprocessing by injecting Adversarial Naturalness-aware Perturbations. In contrast to the prior studies, our proposed method is the first work that generalizes all the existing four watermarking schemes well without involving any model modification, which preserves the fidelity of the target model. We conduct the experiments against four state-of-the-art (SOTA) watermarking schemes on two real tasks (e.g., image classification on ImageNet, face recognition on CelebA) across multiple DNN models. Overall, our proposed AdvNP significantly invalidates the watermarks against the four watermarking schemes on two real-world datasets, i.e., 60.9% on the average attack success rate and up to 97% in the worse case. Moreover, our AdvNP could well survive the image denoising techniques and outperforms the baseline in both the fidelity preserving and watermark removal. Furthermore, we introduce two defense methods to enhance the robustness of DNN watermarking against our AdvNP. Our experimental results pose real threats to the existing watermarking schemes and call for more practical and robust watermarking techniques to protect the copyright of pre-trained DNN models. The source code and models are available at ttps://github.com/GitKJ123/AdvNP.
Wang, Run; Huang, Ziheng; Chen, Zhikai; Liu, Li; Chen, Jing; Wang, Lina
Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations Proceedings Article
In: Raedt, Lud De (Ed.): Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 761–767, International Joint Conferences on Artificial Intelligence Organization, 2022, ISBN: 978-1-956792-00-3, (Main Track).
@inproceedings{Wang2022,
title = {Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations},
author = {Run Wang and Ziheng Huang and Zhikai Chen and Li Liu and Jing Chen and Lina Wang},
editor = {Lud De Raedt},
url = {https://doi.org/10.24963/ijcai.2022/107
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Anti-Forgery-Towards-a-Stealthy-and-Robust-DeepFake-Disruption-Attack-via-Adversarial-Perceptual-aware-Perturbations.-.pdf
},
doi = {10.24963/ijcai.2022/107},
isbn = {978-1-956792-00-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, IJCAI-22},
pages = {761\textendash767},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
abstract = {DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR. In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. Code is available at https://github.com/AbstractTeen/AntiForgery.},
note = {Main Track},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR. In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. Code is available at https://github.com/AbstractTeen/AntiForgery.
Yang, Yang; Chen, Yanjiao; Chen, Fei; Chen, Jing
An Efficient Identity-Based Provable Data Possession Protocol With Compressed Cloud Storage Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1359-1371, 2022, ISSN: 1556-6021.
@article{9733365,
title = {An Efficient Identity-Based Provable Data Possession Protocol With Compressed Cloud Storage},
author = {Yang Yang and Yanjiao Chen and Fei Chen and Jing Chen},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/09/An_Efficient_Identity-Based_Provable_Data_Possession_Protocol_With_Compressed_Cloud_Storage.pdf},
doi = {10.1109/TIFS.2022.3159152},
issn = {1556-6021},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {17},
pages = {1359-1371},
abstract = {Cloud storage is more and more prevalent in practice, and thus how to check its integrity becomes increasingly essential. A classical solution is identity-based (ID-based) provable data possession (PDP), which supports certificateless cloud storage auditing without entire user data. However, existing ID-PDP protocols always require that cloud users outsource data blocks, authenticators and a small-sized file tag to the cloud, and make use of the heavy elliptic curve cryptography over bilinear pairing. These disadvantages would result in vast storage, communication, and computation costs, which is unexpected, especially for resource-limited cloud users. To improve the performance, this paper proposes a novel cryptographic primitive: ID-based PDP with compressed cloud storage. In this model, cloud storage auditing can be achieved by using only encrypted data blocks in a self-verified way, and original data blocks can be reconstructed from the outsourced data. Thus, data owners no longer need to store original data blocks on the cloud. We also use some basic algebraic operations to realize a concrete ID-based PDP protocol with compressed cloud storage, which is quite efficient due to no heavy cryptographic operations involved. The proposed protocol can easily be extended to support the other practical functions by using the primitive replacement technique. The proposed protocol is strictly proven to have the properties of correctness, privacy, unforgeability and detectability. Finally, we give plenty of theoretical analysis and experimental results to validate the efficiency of the proposed protocol.},
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Cloud storage is more and more prevalent in practice, and thus how to check its integrity becomes increasingly essential. A classical solution is identity-based (ID-based) provable data possession (PDP), which supports certificateless cloud storage auditing without entire user data. However, existing ID-PDP protocols always require that cloud users outsource data blocks, authenticators and a small-sized file tag to the cloud, and make use of the heavy elliptic curve cryptography over bilinear pairing. These disadvantages would result in vast storage, communication, and computation costs, which is unexpected, especially for resource-limited cloud users. To improve the performance, this paper proposes a novel cryptographic primitive: ID-based PDP with compressed cloud storage. In this model, cloud storage auditing can be achieved by using only encrypted data blocks in a self-verified way, and original data blocks can be reconstructed from the outsourced data. Thus, data owners no longer need to store original data blocks on the cloud. We also use some basic algebraic operations to realize a concrete ID-based PDP protocol with compressed cloud storage, which is quite efficient due to no heavy cryptographic operations involved. The proposed protocol can easily be extended to support the other practical functions by using the primitive replacement technique. The proposed protocol is strictly proven to have the properties of correctness, privacy, unforgeability and detectability. Finally, we give plenty of theoretical analysis and experimental results to validate the efficiency of the proposed protocol.
Yang, Yang; Chen, Yanjiao; Chen, Fei; Chen, Jing
Identity-Based Cloud Storage Auditing for Data Sharing With Access Control of Sensitive Information Journal Article
In: IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10434-10445, 2022.
@article{9583593,
title = {Identity-Based Cloud Storage Auditing for Data Sharing With Access Control of Sensitive Information},
author = {Yang Yang and Yanjiao Chen and Fei Chen and Jing Chen},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2024/03/Identity-Based_Cloud_Storage_Auditing_for_Data_Sharing_With_Access_Control_of_Sensitive_Information.pdf},
doi = {10.1109/JIOT.2021.3121678},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Internet of Things Journal},
volume = {9},
number = {13},
pages = {10434-10445},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Wang, Mei; He, Kun; Chen, Jing; Li, Zengpeng; Zhao, Wei; Du, Ruiying
Biometrics-Authenticated Key Exchange for Secure Messaging Proceedings Article
In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 2618–2631, 2021.
@inproceedings{WHC+21,
title = {Biometrics-Authenticated Key Exchange for Secure Messaging},
author = {Mei Wang and Kun He and Jing Chen and Zengpeng Li and Wei Zhao and Ruiying Du},
url = {https://doi.org/10.1145/3460120.3484746
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Biometrics-Authenticated-Key-Exchange-for-Secure-Messaging-.pdf},
doi = {10.1145/3460120.3484746},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security},
pages = {2618--2631},
series = {CCS '21},
abstract = {Secure messaging heavily relies on a session key negotiated by an Authenticated Key Exchange (AKE) protocol. However, existing AKE protocols only verify the existence of a random secret key (corresponding to a certificated public key) stored in the terminal, rather than a legal user who uses the messaging application. In this paper, we propose a Biometrics-Authenticated Key Exchange (BAKE) framework, in which a secret key is derived from a user's biometric characteristics that are not necessary to be stored. To protect the privacy of users' biometric characteristics and realize one-round key exchange, we present an Asymmetric Fuzzy Encapsulation Mechanism (AFEM) to encapsulate messages with a public key derived from a biometric secret key, such that only a similar secret key can decapsulate them. To manifest the practicality, we present two AFEM constructions for two types of biometric secret keys and instantiate them with irises and fingerprints, respectively. We perform security analysis of BAKE and show its performance through extensive experiments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Secure messaging heavily relies on a session key negotiated by an Authenticated Key Exchange (AKE) protocol. However, existing AKE protocols only verify the existence of a random secret key (corresponding to a certificated public key) stored in the terminal, rather than a legal user who uses the messaging application. In this paper, we propose a Biometrics-Authenticated Key Exchange (BAKE) framework, in which a secret key is derived from a user's biometric characteristics that are not necessary to be stored. To protect the privacy of users' biometric characteristics and realize one-round key exchange, we present an Asymmetric Fuzzy Encapsulation Mechanism (AFEM) to encapsulate messages with a public key derived from a biometric secret key, such that only a similar secret key can decapsulate them. To manifest the practicality, we present two AFEM constructions for two types of biometric secret keys and instantiate them with irises and fingerprints, respectively. We perform security analysis of BAKE and show its performance through extensive experiments.
Qian, Yongfeng; Ma, Yujun; Chen, Jing; Wu, Di; Tian, Daxin; Hwang, Kai
Optimal Location Privacy Preserving and Service Quality Guaranteed Task Allocation in Vehicle-Based Crowdsensing Networks Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4367-4375, 2021, ISSN: 1558-0016.
@article{9477299,
title = {Optimal Location Privacy Preserving and Service Quality Guaranteed Task Allocation in Vehicle-Based Crowdsensing Networks},
author = {Yongfeng Qian and Yujun Ma and Jing Chen and Di Wu and Daxin Tian and Kai Hwang},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Optimal_Location_Privacy_Preserving_and_Service_Quality_Guaranteed_Task_Allocation_in_Vehicle-Based_Crowdsensing_Networks.pdf},
doi = {10.1109/TITS.2021.3086837},
issn = {1558-0016},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {22},
number = {7},
pages = {4367-4375},
abstract = {With increasing popularity of related applications of mobile crowdsensing, especially in the field of Internet of Vehicles (IoV), task allocation has attracted wide attention. How to select appropriate participants is a key problem in vehicle-based crowdsensing networks. Some traditional methods choose participants based on minimizing distance, which requires participants to submit their current locations. In this case, participants' location privacy is violated, which influences disclosure of participants' sensitive information. Many privacy preserving task allocation mechanisms have been proposed to encourage users to participate in mobile crowdsensing. However, most of them assume that different participants' task completion quality is the same, which is not reasonable in reality. In this paper, we propose an optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. Specifically, we utilize differential privacy to preserve participants' location privacy, where every participant can submit the obfuscated location to the platform instead of the real one. Based on the obfuscated locations, we design an optimal problem to minimize the moving distance and maximize the task completion quality simultaneously. In order to solve this problem, we decompose it into two linear optimization problems. We conduct extensive experiments to demonstrate the effectiveness of our proposed mechanism.},
keywords = {},
pubstate = {published},
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}
With increasing popularity of related applications of mobile crowdsensing, especially in the field of Internet of Vehicles (IoV), task allocation has attracted wide attention. How to select appropriate participants is a key problem in vehicle-based crowdsensing networks. Some traditional methods choose participants based on minimizing distance, which requires participants to submit their current locations. In this case, participants' location privacy is violated, which influences disclosure of participants' sensitive information. Many privacy preserving task allocation mechanisms have been proposed to encourage users to participate in mobile crowdsensing. However, most of them assume that different participants' task completion quality is the same, which is not reasonable in reality. In this paper, we propose an optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. Specifically, we utilize differential privacy to preserve participants' location privacy, where every participant can submit the obfuscated location to the platform instead of the real one. Based on the obfuscated locations, we design an optimal problem to minimize the moving distance and maximize the task completion quality simultaneously. In order to solve this problem, we decompose it into two linear optimization problems. We conduct extensive experiments to demonstrate the effectiveness of our proposed mechanism.
Wang, Xiao; Wang, Zheng; Liu, Wu; Xu, Xin; Chen, Jing; Lin, Chia-Wen
Consistency-Constancy Bi-Knowledge Learning for Pedestrian Detection in Night Surveillance Proceedings Article
In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4463–4471, Association for Computing Machinery, Virtual Event, China, 2021, ISBN: 9781450386517.
@inproceedings{Wang2021,
title = {Consistency-Constancy Bi-Knowledge Learning for Pedestrian Detection in Night Surveillance},
author = {Xiao Wang and Zheng Wang and Wu Liu and Xin Xu and Jing Chen and Chia-Wen Lin},
url = {https://doi.org/10.1145/3474085.3475599
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Consistency-Constancy-Bi-Knowledge-Learning-for-Pedestrian-Detection-in-Night-Surveillance.pdf
},
doi = {10.1145/3474085.3475599},
isbn = {9781450386517},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {4463\textendash4471},
publisher = {Association for Computing Machinery},
address = {Virtual Event, China},
series = {MM '21},
abstract = {Pedestrian detection in the night surveillance is a challenging yet not largely explored task. As the success of the detector in the daytime surveillance and the convenient acquisition of all-weather data, we learn knowledge from these data to benefit pedestrian detection in night surveillance. We find two key properties of surveillance: distribution cross-time consistency and background cross-frame constancy. This paper proposes a consistency-constancy bi-knowledge learning (CCBL) for pedestrian detection in night surveillance, which is able to simultaneously achieve the night pedestrian detection's useful knowledge, coming from day and night surveillance. Firstly, based on the robustness of the existing detector in day surveillance, we obtain pedestrians' distribution in the daytime scene using the detector's detection results in the daytime scene. Based on the consistency of pedestrians' distribution during the day and night in the same scene, the pedestrian distribution from daytime is used as the consistency-knowledge for pedestrian detection in night surveillance. Secondly, the background as a constant knowledge of the surveillance scene is extractable and contributes to the division of the foreground, which contains most of the pedestrian regions and helps in pedestrian detection for night surveillance. Finally, we add bi-knowledge representation to promote each other and merge them together as the final pedestrian representation. Through extensive experiments, our CCBL significantly outperforms the state-of-the-art methods on public pedestrian detection datasets. In the NightSurveillance dataset, CCBL reduced the average missed detection rate by 3.04% compared to the existing best method.},
keywords = {},
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}
Pedestrian detection in the night surveillance is a challenging yet not largely explored task. As the success of the detector in the daytime surveillance and the convenient acquisition of all-weather data, we learn knowledge from these data to benefit pedestrian detection in night surveillance. We find two key properties of surveillance: distribution cross-time consistency and background cross-frame constancy. This paper proposes a consistency-constancy bi-knowledge learning (CCBL) for pedestrian detection in night surveillance, which is able to simultaneously achieve the night pedestrian detection's useful knowledge, coming from day and night surveillance. Firstly, based on the robustness of the existing detector in day surveillance, we obtain pedestrians' distribution in the daytime scene using the detector's detection results in the daytime scene. Based on the consistency of pedestrians' distribution during the day and night in the same scene, the pedestrian distribution from daytime is used as the consistency-knowledge for pedestrian detection in night surveillance. Secondly, the background as a constant knowledge of the surveillance scene is extractable and contributes to the division of the foreground, which contains most of the pedestrian regions and helps in pedestrian detection for night surveillance. Finally, we add bi-knowledge representation to promote each other and merge them together as the final pedestrian representation. Through extensive experiments, our CCBL significantly outperforms the state-of-the-art methods on public pedestrian detection datasets. In the NightSurveillance dataset, CCBL reduced the average missed detection rate by 3.04% compared to the existing best method.
Shen, Lujia; Ji, Shouling; Zhang, Xuhong; Li, Jinfeng; Chen, Jing; Shi, Jie; Fang, Chengfang; Yin, Jianwei; Wang, Ting
Backdoor Pre-Trained Models Can Transfer to All Proceedings Article
In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 3141–3158, 2021.
@inproceedings{SJZ+21,
title = {Backdoor Pre-Trained Models Can Transfer to All},
author = {Lujia Shen and Shouling Ji and Xuhong Zhang and Jinfeng Li and Jing Chen and Jie Shi and Chengfang Fang and Jianwei Yin and Ting Wang},
url = {https://doi.org/10.1145/3460120.3485370
https://datasec.whu.edu.cn/wp-content/uploads/2023/09/Backdoor-Pre-Trained-Models-Can-Transfer-to-All.pdf},
doi = {10.1145/3460120.3485370},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security},
pages = {3141--3158},
abstract = {Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.
Jia, Meng; He, Kun; Chen, Jing; Du, Ruiying; Chen, Weihang; Tian, Zhihong; Ji, Shouling
PROCESS: Privacy-Preserving On-Chain Certificate Status Service Proceedings Article
In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pp. 1–10, 2021.
@inproceedings{JHC+21,
title = {PROCESS: Privacy-Preserving On-Chain Certificate Status Service},
author = {Meng Jia and Kun He and Jing Chen and Ruiying Du and Weihang Chen and Zhihong Tian and Shouling Ji},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/PROCESS_Privacy-Preserving_On-Chain_Certificate_Status_Service.pdf},
doi = {10.1109/INFOCOM42981.2021.9488858},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {IEEE INFOCOM 2021 - IEEE Conference on Computer Communications},
pages = {1--10},
abstract = {Clients (e.g., browsers) and servers require public key certificates to establish secure connections. When a client accesses a server, it needs to check the signature, expiration time, and revocation status of the certificate to determine whether the server is reliable. The existing solutions for checking certificate status either have a long update cycle (e.g., CRL, CRLite) or violate clients’ privacy (e.g., OCSP, CCSP), and these solutions also have the problem of trust concentration. In this paper, we present PROCESS, an online privacy-preserving on-chain certificate status service based on the blockchain architecture, which can ensure decentralized trust and provide privacy protection for clients. Specifically, we design Counting Garbled Bloom Filter (CGBF) that supports efficient queries and BlockOriented Revocation List (BORL) to update CGBF timely in the blockchain. With CGBF, we design a privacy-preserving protocol to protect clients’ privacy when they check the certificate statuses from the blockchain nodes. Finally, we conduct experiments and compare PROCESS with another blockchain-based solution to demonstrate that PROCESS is suitable in practice.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Clients (e.g., browsers) and servers require public key certificates to establish secure connections. When a client accesses a server, it needs to check the signature, expiration time, and revocation status of the certificate to determine whether the server is reliable. The existing solutions for checking certificate status either have a long update cycle (e.g., CRL, CRLite) or violate clients’ privacy (e.g., OCSP, CCSP), and these solutions also have the problem of trust concentration. In this paper, we present PROCESS, an online privacy-preserving on-chain certificate status service based on the blockchain architecture, which can ensure decentralized trust and provide privacy protection for clients. Specifically, we design Counting Garbled Bloom Filter (CGBF) that supports efficient queries and BlockOriented Revocation List (BORL) to update CGBF timely in the blockchain. With CGBF, we design a privacy-preserving protocol to protect clients’ privacy when they check the certificate statuses from the blockchain nodes. Finally, we conduct experiments and compare PROCESS with another blockchain-based solution to demonstrate that PROCESS is suitable in practice.
He, Kun; Chen, Jing; Zhou, Qinxi; Du, Ruiying; Xiang, Yang
Secure Dynamic Searchable Symmetric Encryption With Constant Client Storage Cost Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1538–1549, 2021.
@article{HCZ+21,
title = {Secure Dynamic Searchable Symmetric Encryption With Constant Client Storage Cost},
author = {Kun He and Jing Chen and Qinxi Zhou and Ruiying Du and Yang Xiang},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/Secure_Dynamic_Searchable_Symmetric_Encryption_With_Constant_Client_Storage_Cost.pdf},
doi = {10.1109/TIFS.2020.3033412},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {16},
pages = {1538--1549},
abstract = {Dynamic Searchable Symmetric Encryption (DSSE) enables users to search on the encrypted database stored on a semi-trusted server while keeping the search and update information under acceptable leakage. However, most existing DSSE schemes are not efficient enough in practice due to the complex structures and cryptographic primitives. Moreover, the storage cost on the client side grows linearly with the number of keywords in the database, which induces unaffordable storage cost when the size of keyword set is large. In this article, we focus on secure dynamic searchable symmetric encryption with constant client storage cost. Our framework is boosted by fish-bone chain, a novel two-level structure which consists of Logical Keyword Index Chain (LoKIC) and Document Index Chain (DIC). To instantiate the proposed framework, we propose a forward secure DSSE scheme, called CLOSE-F, and a forward and backward secure DSSE scheme, called CLOSE-FB. Experiments showed that the computation cost of CLOSE-F and CLOSE-FB are as efficient as the state-of-the-art solutions, while the storage costs on the client side are constant in both CLOSE-F and CLOSE-FB, which are much smaller than existing schemes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dynamic Searchable Symmetric Encryption (DSSE) enables users to search on the encrypted database stored on a semi-trusted server while keeping the search and update information under acceptable leakage. However, most existing DSSE schemes are not efficient enough in practice due to the complex structures and cryptographic primitives. Moreover, the storage cost on the client side grows linearly with the number of keywords in the database, which induces unaffordable storage cost when the size of keyword set is large. In this article, we focus on secure dynamic searchable symmetric encryption with constant client storage cost. Our framework is boosted by fish-bone chain, a novel two-level structure which consists of Logical Keyword Index Chain (LoKIC) and Document Index Chain (DIC). To instantiate the proposed framework, we propose a forward secure DSSE scheme, called CLOSE-F, and a forward and backward secure DSSE scheme, called CLOSE-FB. Experiments showed that the computation cost of CLOSE-F and CLOSE-FB are as efficient as the state-of-the-art solutions, while the storage costs on the client side are constant in both CLOSE-F and CLOSE-FB, which are much smaller than existing schemes.
He, Kun; Chen, Jing; Yuan, Quan; Ji, Shouling; He, Debiao; Du, Ruiying
Dynamic Group-Oriented Provable Data Possession in the Cloud Journal Article
In: IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 3, pp. 1394–1408, 2021.
@article{HCY+21,
title = {Dynamic Group-Oriented Provable Data Possession in the Cloud},
author = {Kun He and Jing Chen and Quan Yuan and Shouling Ji and Debiao He and Ruiying Du},
url = {https://datasec.whu.edu.cn/wp-content/uploads/2023/08/Dynamic_Group-Oriented_Provable_Data_Possession_in_the_Cloud.pdf},
doi = {10.1109/TDSC.2019.2925800},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
volume = {18},
number = {3},
pages = {1394--1408},
abstract = {As an important security property of cloud storage, data integrity has not been sufficiently studied under the multi-writer model, where a group of users work on shared files collaboratively and any group member can update the data by modification, insertion, and deletion operations. Existing works under such multi-writer model would bring large storage cost to the third-party verifiers. Furthermore, to the best of our knowledge, none of the existing works for shared files supports fully dynamic operations, which implies that users cannot freely perform the update operations. In this paper, we propose the first public auditing scheme for shared data that supports fully dynamic operations and achieves constant storage cost for the verifiers. Our scheme, named PRAYS, is boosted by a new paradigm for remote data integrity checking. To implement the new paradigm, we proposed a specially designed authenticated structure, called blockless Merkle tree, and a novel cryptographic primitive, called permission-based signature. Extensive evaluation demonstrates that PRAYS is as efficient as the existing less-functional solutions. We believe that PRAYS is an important step towards designing practical multi-writer cloud storage systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As an important security property of cloud storage, data integrity has not been sufficiently studied under the multi-writer model, where a group of users work on shared files collaboratively and any group member can update the data by modification, insertion, and deletion operations. Existing works under such multi-writer model would bring large storage cost to the third-party verifiers. Furthermore, to the best of our knowledge, none of the existing works for shared files supports fully dynamic operations, which implies that users cannot freely perform the update operations. In this paper, we propose the first public auditing scheme for shared data that supports fully dynamic operations and achieves constant storage cost for the verifiers. Our scheme, named PRAYS, is boosted by a new paradigm for remote data integrity checking. To implement the new paradigm, we proposed a specially designed authenticated structure, called blockless Merkle tree, and a novel cryptographic primitive, called permission-based signature. Extensive evaluation demonstrates that PRAYS is as efficient as the existing less-functional solutions. We believe that PRAYS is an important step towards designing practical multi-writer cloud storage systems.