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26th International Conference on Information and Communications Security, ICICS 2024

作者： Miao, Weijie Wu, Wenyuan Chongqing Key Laboratory of Secure Computing for Biology Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9789819787975

In the field of big data, logistic regression for binary and multi-class classification is widely used. Nowadays, there is growing concern about data privacy protection issues. This paper focuses on scenarios involving two parties participating and data being horizontally distributed. Based on homomorphic encryption, a logistic regression model training scheme is designed. This scheme reduces the number of iterations in the training process by using the second-order approximation Newton’s method. It employs the conjugate gradient method to solve the Newton’s method, and introduces a small amount of interaction to reduce ciphertext domain division operations, greatly reducing the computational overhead of the ciphertext domain. Additionally, a new encoding method is used to reduce the number of ciphertext multiplications and communication overhead. Furthermore, the "One-vs-Rest" decomposition strategy is adopted, combined with SIMD(Single Instruction, Multiple Data) technology, extending the binary model to multi-classification. Experimental results show that with the use of Privacy-preserving Logistic Regression scheme (PPLR), for most datasets, setting the number of iterations to within 3 rounds can achieve accuracy comparable to existing privacy protection schemes of 5 to 7 rounds. For sample datasets with 60 and 112 dimensions, existing similar schemes require 90 s and 165 s to complete 5 rounds of iteration, while this scheme only requires 8s and 27s. Moreover, the communication overhead is reduced to half of the original scheme, requiring only 30.8MB and 62.7MB to complete training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Membership Data Privacy Protection and Poisoning Detection Scheme for Federated Learning 1st

Membership Data Privacy Protection and Poisoning Detection ...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Li, Yafeng Sun, Zhijun Ma, Lichuan China CETC Key Laboratory of Technology on Data Link Xi’an710068 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China

ISBN: (纸本)9789819794119

In the era of big data, data privacy and security are highly valued, leading to the phenomenon of "data islands", which hinders the effective utilization of data. Federated learning, as a privacy-preserving distributed machine learning method, allows model training without sharing raw data, addressing the privacy concerns in cross-organizational data cooperation. Although federated learning protects data privacy, participants’ gradient parameters can still potentially leak sensitive information. As a result, extensive research has focused on conducting federated learning while protecting the privacy of gradient parameters and reducing the communication and computational overhead for participants. These efforts assume that each participant adheres honestly to the protocol;however, malicious submissions of incorrect gradient parameters can significantly bias the federated learning model. This paper proposes a federated learning scheme for privacy-protected gradient aggregation and poison detection that defends against malicious poisoning attacks while safeguarding data privacy, and optimizes computational and communication overheads. By employing inner product triples, the computation process is further optimized, enhancing system performance. The security of the scheme is demonstrated, and its efficiency is validated through practical tests, showing that the optimized solution significantly outperforms the basic scheme in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Multi-Level Queue Security in Switches: Adversarial Inference and Differential Privacy Protection in SDN

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Chinese Journal of Electronics 2025年第2期34卷 533-547页

作者： Dong, Xuewen Xue, Lingtao Zhang, Tao You, Zhichao Li, Guangxia Shen, Yulong School of Computer Science & Technology Xidian University Xi'an710071 China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710126 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710126 China

Network switches are critical elements in any network infrastructure for traffic forwarding and packet priority scheduling, which naturally become a target of network adversaries. Most attacks on switches focus on purposely forwarding packets to the wrong network nodes or generating flooding. However, potential privacy leakage in the multi-level priority queue of switches has not been considered. In this paper, we are the first to discuss the multi-level priority queue security and privacy protection problem in switches. Observing that packet leaving order from a queue is strongly correlated to its priority, we introduce a policy inference attack that exploits specific priority-mapping rules between different packet priorities and priority sub-queues in the multi-level queues. Next, based on the policy inference result and the built-in traffic shaping strategy, a capacity inference attack with the error probability decaying exponentially in the number of attacks is presented. In addition, we propose a differentially private priority scheduling mechanism to defend against the above attacks in OpenFlow switches. Theoretical analysis proves that our proposed mechanism can satisfy Ε-differential privacy. Extensive evaluation results show that our mechanism can defend against inference attacks well and achieves up to 2.7 times priority process efficiency than a random priority scheduling strategy. © 2015 Chinese Institute of Electronics.

关键词： Queueing networks

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on Computers 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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Differential Fault Attack on GIFT

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Chinese Journal of Electronics 2021年第4期30卷 669-675页

作者： XIE Min TIAN Feng LI Jiaqi State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing

GIFT, a lightweight block cipher proposed at CHES2017, has been widely cryptanalyzed this *** paper studies the differential diffusion characteristics of round function of GIFT at first, and proposes a random nibble-based differential fault attack. The key recovery scheme is developed on the statistical properties we found for the differential distribution table of the S-box. A lot of experiments had been done and experimental results show that one round key can be retrieved with an average of20.24 and 44.96 fault injections for GIFT-64 and GIFT-128 respectively. Further analysis shows that a certain number of fault injections recover most key bits. So we demonstrate an improved fault attack combined with the method of exhaustive search, which shows that the master key can be recovered by performing 216and 217computations and injecting 31 and 32 faults on an average for GIFT-64 and GIFT-128 respectively.

关键词： GIFT Lightweight block cipher Differential fault attack Key recovery

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Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

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Chinese Journal of Electronics 2024年第1期33卷 30-42页

作者： Hongning LI Tonghui HU Jiexiong CHEN Xiuqiang WU Qingqi PEI Guangzhou Institute of Technology Xidian University Guangzhou CEPREI Shaanxi Key Laboratory of Blockchain and Secure Computing

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users' privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users' location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.

关键词： Location privacy preservation Cognitive radio network Cooperative spectrum sensing

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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Efficient and Privacy-Enhancing Non-Interactive Periocular Authentication for Access Control Services

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IEEE Transactions on Network and Service Management 2025年

作者： Li, Yating Wang, Le Xue, Liang Liu, Jingwei Lin, Xiaodong Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Guelph School of Computer Science GuelphN1G 2W1 Canada York University School of Information Technology North YorkM3J 1P3 Canada

Periocular authentication has emerged as an increasingly prominent approach in access control services, especially in situations of face occlusion. However, its limited feature area and low information complexity make it susceptible to tampering and identity forgery. To address these vulnerabilities, we propose a practical privacy-enhancing non-interactive periocular authentication scheme for access control services. It enables encrypted cosine similarity matching on an untrusted remote authentication server by leveraging random masking (RM) and dual-trapdoor homomorphic encryption (DTHE). Additionally, a weight control matrix is introduced to enhance authentication accuracy by assigning importance to different feature dimensions. To accommodate devices with varying trust levels, we employ adaptive authentication strategies. For trusted mobile devices, we implement secure single-factor authentication based on periocular features, while for external devices with unknown security status, we enforce a two-factor authentication mechanism integrating periocular features with tokens to mitigate unauthorized access risks. Additionally, our scheme conceals users’ true identities from external devices during authentication. Security analysis demonstrates that our solution effectively mitigates tampering and replay attacks in the network while preventing privacy leakage. As validated by experimental results, the proposed scheme enables efficient authentication of obscured faces. © 2004-2012 IEEE.

关键词： Differential privacy

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

关键词： Concurrency control

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

关键词： Graph neural networks

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