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学者姓名：刘西蒙
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A large-scale model is typically trained on an extensive dataset to update its parameters and enhance its classification capabilities. However, directly using such data can raise significant privacy concerns, especially in the medical field, where datasets often contain sensitive patient information. Federated Learning (FL) offers a solution by enabling multiple parties to collaboratively train a high-performance model without sharing their raw data. Despite this, during the federated training process, attackers can still potentially extract private information from local models. To bolster privacy protections, Differential Privacy (DP) has been introduced to FL, providing stringent safeguards. However, the combination of DP and data heterogeneity can often lead to reduced model accuracy. To tackle these challenges, we introduce a sampling-memory mechanism, FedSam, which improves the accuracy of the global model while maintaining the required noise levels for differential privacy. This mechanism also mitigates the adverse effects of data heterogeneity in heterogeneous federated environments, thereby improving the global model's overall performance. Experimental evaluations on datasets demonstrate the superiority of our approach. FedSam achieves a classification accuracy of 95.03%, significantly outperforming traditional DP-FedAvg (91.74%) under the same privacy constraints, highlighting FedSam's robustness and efficiency.
Keyword ：
Data heterogeneity Data heterogeneity Differential privacy Differential privacy Federated learning Federated learning
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| GB/T 7714 | Li, Hongtao , Li, Xinyu , Liu, Ximeng et al. FedSam: Enhancing federated learning accuracy with differential privacy and data heterogeneity mitigation [J]. | COMPUTER STANDARDS & INTERFACES , 2026 , 95 . |
| MLA | Li, Hongtao et al. "FedSam: Enhancing federated learning accuracy with differential privacy and data heterogeneity mitigation" . | COMPUTER STANDARDS & INTERFACES 95 (2026) . |
| APA | Li, Hongtao , Li, Xinyu , Liu, Ximeng , Wang, Bo , Wang, Jie , Tian, Youliang . FedSam: Enhancing federated learning accuracy with differential privacy and data heterogeneity mitigation . | COMPUTER STANDARDS & INTERFACES , 2026 , 95 . |
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为空间众包中的工人分配任务是后续收集位置相关数据的重要前提 . 为了应对可能的位置隐私泄露 问题，研究者往往结合地理不可区分性进行保护. 现有满足地理不可区分性的任务分配方法通常针对一对一场景，其 研究目标一般集中在最小化平均旅行距离，而不是最大化任务分配数量；同时，它们假设工人能分配去执行任意的任 务. 此外，这些研究往往结合平面拉普拉斯机制实现地理不可区分性. 上述机制的随机性和无界性会导致工人上传的 位置数据包含过量噪音，进而降低任务分配的效用，导致工人平均旅行距离较大或者任务无法完全分配. 为解决以上 问题，本文提出满足地理不可区分性的偏好感知多对多任务分配算法 MONITOR（Many-to-many task allOcation under geo-iNdIsTinguishability for spatial crOwdsouRcing）. 该算法主要思想是对工人的偏好任务进行分组加噪并上传工人真 实位置到模糊偏好任务位置之间的距离以代替直接上传工人的模糊位置. 在MONITOR中，为了收集任务分配必需的 工人到任务的距离信息，设计了基于分组的模糊距离收集方法 GroCol（Group-based obfuscated distance Collection）；同 时为了提高任务分配的效用，设计了参数无关的模糊距离比较方法ParCom（Parameter-free obfuscated distance Compari⁃ son）. 此外，本文进一步从理论上分析了MONITOR的隐私、效用和复杂度. 在2个真实数据集和1个模拟数据集上的 实验结果表明MONITOR取得与非隐私任务分配类似的任务分配数量，且较基准方法的旅行距离降低了20%以上.
Keyword ：
任务分配 任务分配 地理不可区分性 地理不可区分性 平均旅行距离 平均旅行距离 空间众包 空间众包 隐私保护 隐私保护
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| GB/T 7714 | 张朋飞 , 翟睿辰 , 程 祥 et al. 满足地理不可区分性的偏好感知多对多 任务分配算法 [J]. | 电子学报 , 2025 . |
| MLA | 张朋飞 et al. "满足地理不可区分性的偏好感知多对多 任务分配算法" . | 电子学报 (2025) . |
| APA | 张朋飞 , 翟睿辰 , 程 祥 , 张治坤 , 刘西蒙 , 王 杰 . 满足地理不可区分性的偏好感知多对多 任务分配算法 . | 电子学报 , 2025 . |
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针对区块链技术在区块大小和生成速率上的固有限制导致的可扩展性问题,支付通道网络(pay-ment channel network,PCN)提供了链下扩容的有效方案.然而,传统的使用可锁定结构的PCN存在以下两个缺点:存在作恶方时该结构只能结束交易过程,却无法对恶意行为者实施识别与惩罚;某一笔交易需要复盘时,PCN中出现的所有交易都需要恢复,导致巨大的计算开销.鉴于此,提出了一种基于可追责断言性能优化的支付通道网络方案——AAPO-PCN(accountable assertions performance optimization-payment channel network).AAPO-PCN通过引进可追责断言算法,构建了一种可编辑的Merkle树结构.不同于传统Merkle树,该方案采用变色龙哈希函数替换原有哈希算法,并整合可追责断言机制,旨在有效识别恶意用户.通过这种方式,不仅令相关交易的恢复更加高效,同时也大幅减少了计算开销.最后提供了全面的安全性分析与实验,结果表明,AAPO-PCN在不牺牲安全性的情况下,具有更优的计算效率与通信开销.
Keyword ：
Merkle树 Merkle树 变色龙哈希函数 变色龙哈希函数 可追责断言 可追责断言 支付通道网络 支付通道网络
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| GB/T 7714 | 李雯琪 , 应作斌 , 臧嘉威 et al. 基于可追责断言的支付通道网络性能优化研究 [J]. | 网络与信息安全学报 , 2025 , 11 (1) : 66-78 . |
| MLA | 李雯琪 et al. "基于可追责断言的支付通道网络性能优化研究" . | 网络与信息安全学报 11 . 1 (2025) : 66-78 . |
| APA | 李雯琪 , 应作斌 , 臧嘉威 , 熊金波 , 刘西蒙 . 基于可追责断言的支付通道网络性能优化研究 . | 网络与信息安全学报 , 2025 , 11 (1) , 66-78 . |
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Next Point-of-Interest (POI) recommendation plays an important role in various Location-Based Social Networks (LBSNs). It main objective is to predict the user's next interested POI based on previous check-in information. Most existing research treats next POI recommendation as a sequence prediction problem, ignoring collaborative signals from other users as well as security. Instead, a Differential Privacy-based Transformer Framework for Next POI Recommendation (LDPTRec) is proposed, which focuses on dynamic privacy protection, category-specific temporal modeling, and privacy-preserving sequence integration. (1) A privacy trajectory flow graph is constructed by using social-aware edge local differential privacy to protect users behavioral and location privacy. (2) A novel temporal category-aware context embedding algorithm is designed to capture diverse temporal patterns of POI categories. (3) A DP-Transformer algorithm with theoretical privacy guarantees, validated by experiments showing 5.6% accuracy gains and 35.17% lower cold-start latency. Ablation studies validate its components effectiveness, and time-cost experiments confirm its enhanced recommendation efficiency. Overall, LDPTRec effectively balances recommendation security and efficiency while improving accuracy. © 2004-2012 IEEE.
Keyword ：
Differential Privacy Differential Privacy Location-Based Social Network Location-Based Social Network Next POI Recommendation Next POI Recommendation Privacy Protection Privacy Protection Transformer Transformer
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| GB/T 7714 | Huang, Y. , Liu, X. , Miao, Y. et al. LDPTRec: A Differential Privacy based Transformer Framework for Next POI Recommendation [J]. | IEEE Transactions on Dependable and Secure Computing , 2025 . |
| MLA | Huang, Y. et al. "LDPTRec: A Differential Privacy based Transformer Framework for Next POI Recommendation" . | IEEE Transactions on Dependable and Secure Computing (2025) . |
| APA | Huang, Y. , Liu, X. , Miao, Y. , Zhang, J. , Cheng, D. . LDPTRec: A Differential Privacy based Transformer Framework for Next POI Recommendation . | IEEE Transactions on Dependable and Secure Computing , 2025 . |
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Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented; 2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods.
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| GB/T 7714 | Yin, Jia-Li , Wang, Weijian , Lyhwa et al. Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks [J]. | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 9 , 2025 : 9508-9516 . |
| MLA | Yin, Jia-Li et al. "Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks" . | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 9 (2025) : 9508-9516 . |
| APA | Yin, Jia-Li , Wang, Weijian , Lyhwa , Lin, Wei , Liu, Ximeng . Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks . | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 9 , 2025 , 9508-9516 . |
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Deep neural networks (DNNs) achieve remarkable results in image recognition, speech recognition, and other fields. However, their vulnerability to adversarial examples raises significant security concerns. Adversarial examples cause model misjudgment by introducing minimal perturbations to original samples. In the realm of current adversarial attack research, although traditional iterative attacks are used to generate adversarial examples, they still falter in target migration tasks. To address this, we propose a Self-Universal Cognitive Patch Attack (SUCPA) to boost the success rate of transfer-based targeted attacks. Our approach is motivated by the finding that generalized adversarial perturbations can enhance target migration attack performance. SUCPA innovatively avoids extra data usage, optimizing adversarial perturbations by maximizing feature similarity between adversarial global images and randomly cropped local regions. Specifically, we introduce a domainagnostic attention module and a patch attack module with weight decay to leverage robust intermediate feature representations, aggregate gradient information, and improve perturbation generality. Experiments on the ImageNet dataset demonstrate that SUCPA outperforms existing gradient-based attack methods on both standard and robustly trained models. © 2025 IEEE.
Keyword ：
Generative adversarial networks Generative adversarial networks Image recognition Image recognition Iterative methods Iterative methods Network security Network security Speech communication Speech communication Speech recognition Speech recognition
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| GB/T 7714 | Chen, Xiaochu , Liu, Ximeng . Self-Universal Cognitive Patch Attack for Targeted Transfer [C] . 2025 . |
| MLA | Chen, Xiaochu et al. "Self-Universal Cognitive Patch Attack for Targeted Transfer" . (2025) . |
| APA | Chen, Xiaochu , Liu, Ximeng . Self-Universal Cognitive Patch Attack for Targeted Transfer . (2025) . |
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Abstract ：
Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented; 2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Backpropagation Backpropagation Deep neural networks Deep neural networks
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| GB/T 7714 | Yin, Jia-Li , Wang, Weijian , Lyhwa et al. Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks [C] . 2025 : 9508-9516 . |
| MLA | Yin, Jia-Li et al. "Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks" . (2025) : 9508-9516 . |
| APA | Yin, Jia-Li , Wang, Weijian , Lyhwa , Lin, Wei , Liu, Ximeng . Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks . (2025) : 9508-9516 . |
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Data confidentiality, a fundamental security element for dependable cloud storage, has been drawing widespread concern. Public-key encryption with keyword search (PEKS) has emerged as a promising approach for privacy protection while enabling efficient retrieval of encrypted data. One of the typical applications of PEKS is searching sensitive electronic medical records (EMR) in healthcare clouds. However, many traditional countermeasures fall short of balancing privacy protection with search efficiency, and they often fail to support multi-user EMR sharing. To resolve these challenges, we propose a novel lightweight multi-user public-key authenticated encryption scheme with keyword search (LM-PAEKS). Our design effectively counters the inside keyword guessing attack (IKGA) while maintaining the sizes of ciphertext and trapdoor constant in multi-user scenarios. The novelty of our approach relies on introducing a dedicated receiver server that skillfully transforms the complex many-to-many relationship between senders and receivers into a streamlined one-to-one relationship. This transformation prevents the sizes of ciphertext and trapdoor from scaling linearly with the number of participants. Our approach ensures ciphertext indistinguishability and trapdoor privacy while avoiding bilinear pairing operations on the client side. Comparative performance analysis demonstrates that LM-PAEKS features significant computational efficiency while meeting higher security requirements, positioning it as a robust alternative to existing solutions.
Keyword ：
Cloud computing Cloud computing Encryption Encryption Hospitals Hospitals Indexes Indexes inside keyword guessing attack inside keyword guessing attack Keyword search Keyword search lightweight cryptography lightweight cryptography multi-user healthcare cloud multi-user healthcare cloud Privacy Privacy Public key Public key public-key authenticated encryption public-key authenticated encryption Receivers Receivers Searchable encryption Searchable encryption Security Security Servers Servers
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| GB/T 7714 | Xu, Yongliang , Cheng, Hang , Li, Jiguo et al. Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 : 3234-3246 . |
| MLA | Xu, Yongliang et al. "Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 20 (2025) : 3234-3246 . |
| APA | Xu, Yongliang , Cheng, Hang , Li, Jiguo , Liu, Ximeng , Zhang, Xinpeng , Wang, Meiqing . Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 , 3234-3246 . |
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Edge storage is becoming an increasingly appealing alternative for data owners (DOs), offering benefits like decreased latency and minimized bandwidth usage compared to traditional cloud storage solutions. Nonetheless, stored data within edge servers (ESs) remains vulnerable to disruptions. Existing data integrity auditing schemes face challenges such as the costs of third-party auditors (TPA), unreliable and delayed audit results, and effective management of data inspection concerning time and energy consumption. To tackle these challenges, we introduce DIVO, a decentralized data inspection approach. DIVO leverages ESs as each others' auditors, removing the necessity for a centralized party, thereby mitigating collision risks and potential biases in audit results. We propose a game-theoretic technique to efficiently manage data inspection and verification offloading to ESs. By formulating the decision-making issue as a strategic game for optimally allocating verification tasks among multiple ESs, we establish the presence of Nash equilibrium and design a strategy to attain it. Through comprehensive security and performance evaluations, DIVO has been shown to operate securely within the random oracle model while delivering notable efficiency improvements over recent methods. Our analysis highlights that DIVO surpasses a wide range of recent approaches in both communication and computation efficiency.
Keyword ：
Blockchains Blockchains Cloud computing Cloud computing Costs Costs Data integrity Data integrity Data integrity auditing Data integrity auditing edge storage edge storage Faces Faces Games Games game theory game theory Inspection Inspection Nash equilibrium Nash equilibrium provable security provable security Security Security Servers Servers
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| GB/T 7714 | Seyedi, Zahra , Rahmati, Farhad , Ali, Mohammad et al. Decentralized Data Integrity Inspection Offloading in Edge Computing Systems Using Potential Games [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (5) : 3950-3961 . |
| MLA | Seyedi, Zahra et al. "Decentralized Data Integrity Inspection Offloading in Edge Computing Systems Using Potential Games" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 24 . 5 (2025) : 3950-3961 . |
| APA | Seyedi, Zahra , Rahmati, Farhad , Ali, Mohammad , Liu, Ximeng . Decentralized Data Integrity Inspection Offloading in Edge Computing Systems Using Potential Games . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (5) , 3950-3961 . |
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In the realm of secure data outsourcing, verifiable dynamic searchable symmetric encryption (VDSSE) enables a client to verify search results obtained from an untrusted server while protecting the data privacy. Nevertheless, the storage cost of verification structure in some schemes escalates linearly with the number of keywords, and the generation of proofs demands a substantial number of exponentiation operations. Moreover, some schemes overlook forward and backward security in the dynamic database. In this article, we introduce FB-VDSSE, an advanced VDSSE scheme that ensures both forward and backward security. Specifically, we introduce an efficient accumulation commitment verification structure (AC-VS) that attains a commitment verification value with a constant-size storage cost. Based on the AC-VS, we further propose a forward and backward secure VDSSE scheme. Within this scheme, the server exclusively generates a membership proof at the corresponding index of the vector, reducing the computation cost associated with the search operation. Finally, we provide the security proof and functional comparison, demonstrating that our scheme effectively ensures forward security, backward security, and verifiability. Additionally, the experimental evaluations underscore the efficiency of our scheme, showcasing its superior performance compared to relevant schemes in practical scenarios.
Keyword ：
Backward security Backward security forward security forward security searchable symmetric encryption searchable symmetric encryption verifiability verifiability
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| GB/T 7714 | Zhao, Chenbin , Du, Ruiying , He, Kun et al. Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (3) : 2633-2645 . |
| MLA | Zhao, Chenbin et al. "Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security" . | IEEE INTERNET OF THINGS JOURNAL 12 . 3 (2025) : 2633-2645 . |
| APA | Zhao, Chenbin , Du, Ruiying , He, Kun , Chen, Jing , Li, Jiguo , Liu, Ximeng et al. Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (3) , 2633-2645 . |
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