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Recruiting users in mobile crowdsensing (MCS) can make the platform obtain high-quality data to provide better services. Although the privacy leakage during the process of user recruitment has received a lot of research attention, none of the existing work considers the evaluation of the sensing quality of privacy-preserving data submitted by users, which makes the platform incapable of recruiting users suitably to obtain high-quality sensing data, thereby reducing the reliability of MCS services. To solve this problem, we first propose a sensing quality evaluation method based on the deviation and variance of sensing data. According to it, the platform can obtain the sensing quality of privacy-preserving data for each user during the recruitment. Then we model the user recruitment with a limited budget platform as a Combinatorial Multi-Armed Bandit (CMAB) game to determine the recruited users based on the sensing quality of data obtained by evaluation. Finally, we theoretically prove that our algorithm satisfies differential privacy and the upper bound on the regret of rewards is restricted. Experimental results show that our proposal is superior in various properties, and our method has a 73.67% advantage in accumulated sensing qualities compared with comparison schemes.
Keyword ：
combinatorial multi-armed bandit combinatorial multi-armed bandit Differential privacy Differential privacy Mobile crowdsensing Mobile crowdsensing Perturbation methods Perturbation methods Privacy Privacy privacy protection privacy protection Protection Protection Recruitment Recruitment Sensors Sensors Task analysis Task analysis user recruitment user recruitment
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| GB/T 7714 | An, Jieying , Ren, Yanbing , Li, Xinghua et al. Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing [J]. | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) : 787-803 . |
| MLA | An, Jieying et al. "Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing" . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 22 . 1 (2025) : 787-803 . |
| APA | An, Jieying , Ren, Yanbing , Li, Xinghua , Zhang, Man , Luo, Bin , Miao, Yinbin et al. Privacy-Preserving User Recruitment With Sensing Quality Evaluation in Mobile Crowdsensing . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) , 787-803 . |
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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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Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate most sample information for reconstruction. Besides, existing attacks embed additional sample information into gradients to improve the attack effect and cause gradient expansion, leading to a more severe gradient clipping in FL with LDP. In this paper, we propose a sample reconstruction attack against LDP-based FL with any target models to reconstruct victims' sensitive samples to illustrate that FL with LDP is not flawless. Considering gradient expansion in reconstruction attacks and noise in LDP, the core of the proposed attack is gradient compression and reconstructed sample denoising. For gradient compression, an inference structure based on sample characteristics is presented to reduce redundant gradients against LDP. For reconstructed sample denoising, we artificially introduce zero gradients to observe noise distribution and scale confidence interval to filter the noise. Theoretical proof guarantees the effectiveness of the proposed attack. Evaluations show that the proposed attack is the only attack that reconstructs victims' training samples in LDP-based FL and has little impact on the target model's accuracy. We conclude that LDP-based FL needs further improvements to defend against sample reconstruction attacks effectively.
Keyword ：
Accuracy Accuracy Complexity theory Complexity theory data privacy data privacy differential privacy differential privacy Federated learning (FL) Federated learning (FL) Generative adversarial networks Generative adversarial networks Generators Generators Image reconstruction Image reconstruction Measurement Measurement Noise Noise Privacy Privacy Servers Servers Training Training
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| GB/T 7714 | You, Zhichao , Dong, Xuewen , Li, Shujun et al. Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 : 1519-1534 . |
| MLA | You, Zhichao et al. "Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 20 (2025) : 1519-1534 . |
| APA | You, Zhichao , Dong, Xuewen , Li, Shujun , Liu, Ximeng , Ma, Siqi , Shen, Yulong . Local Differential Privacy Is Not Enough: A Sample Reconstruction Attack Against Federated Learning With Local Differential Privacy . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2025 , 20 , 1519-1534 . |
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Federated Learning (FL) is an efficient, distributed machine learning paradigm that enables multiple clients to jointly train high-performance deep learning models while maintaining training data locally. However, due to its distributed computing nature, malicious clients can manipulate the prediction of the trained model through backdoor attacks. Existing defense methods require significant computational and communication overhead during the training or testing phases, limiting their practicality in resource-constrained scenarios and being unsuitable for the Non-IID data distribution typical in general FL scenarios. To address these challenges, we propose the FedPD framework, in which servers and clients exchange prototypes rather than model parameters, preventing the implantation of backdoor channels by malicious clients during FL training and effectively eliminating the success of backdoor attacks at the source, significantly reducing communication overhead. Additionally, prototypes can serve as global knowledge to correct clients' local training. Experiments and performance analysis show that FedPD achieves superior and consistent defense performance compared to existing representative approaches against backdoor attacks. In specific scenarios, FedPD can reduce the success rate of attacks by 90.73% compared to FedAvg without defense while maintaining the main task accuracy above 90%.
Keyword ：
Backdoor attacks Backdoor attacks Federated learning Federated learning Non-IID data Non-IID data Prototypical networks Prototypical networks
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| GB/T 7714 | Tan, Zhou , Cai, Jianping , Li, De et al. FedPD: Defending federated prototype learning against backdoor attacks [J]. | NEURAL NETWORKS , 2025 , 184 . |
| MLA | Tan, Zhou et al. "FedPD: Defending federated prototype learning against backdoor attacks" . | NEURAL NETWORKS 184 (2025) . |
| APA | Tan, Zhou , Cai, Jianping , Li, De , Lian, Puwei , Liu, Ximeng , Che, Yan . FedPD: Defending federated prototype learning against backdoor attacks . | NEURAL NETWORKS , 2025 , 184 . |
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The backdoor attack has emerged as a prevalent threat that affects the effectiveness of machine learning models in intelligent vehicles. While such attacks may not impair the normal performance of the trained model, they can be exploited by malicious entities to manipulate model inferences, resulting in serious problems. In this paper, we design a dynamic gradient clipping (DGC) method aimed at rectifying backdoor models by eliminating the underlying backdoor trigger. Firstly, we construct a repair dataset fused by some clean samples and few-shot backdoor samples to amplify the backdoor behavior when we only obtain limited backdoor samples. Subsequently, we introduce sample states to characterize the backdoor behavior of the target model, determined by the model's inference outcome. Finally, we devise the DGC method to clip parameter gradients at varying degrees, effectively eliminating the backdoor trigger within the target model. Through the evaluation, the simulation results demonstrate that our DGC method exhibits robust defense capabilities against four contemporary state-of-the-art backdoor attacks, reducing the attack success rate by 95% with only 0.1% similar to 4.8% model accuracy loss.
Keyword ：
backdoor attack backdoor attack dynamic gradient clipping dynamic gradient clipping few-shot model repairing few-shot model repairing intelligent vehicle intelligent vehicle Machine learning Machine learning
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| GB/T 7714 | Ma, Xindi , Li, Xinfu , Zhang, Junying et al. Repairing Backdoor Model With Dynamic Gradient Clipping for Intelligent Vehicles [J]. | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) : 804-818 . |
| MLA | Ma, Xindi et al. "Repairing Backdoor Model With Dynamic Gradient Clipping for Intelligent Vehicles" . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 22 . 1 (2025) : 804-818 . |
| APA | Ma, Xindi , Li, Xinfu , Zhang, Junying , Ma, Zhuo , Jiang, Qi , Liu, Ximeng et al. Repairing Backdoor Model With Dynamic Gradient Clipping for Intelligent Vehicles . | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING , 2025 , 22 (1) , 804-818 . |
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Due to the complexity of integrated circuit design and manufacturing process, an increasing number of third parties are outsourcing their untrusted intellectual property (IP) cores to pursue greater economic benefits, which may embed numerous security issues. The covert nature of hardware Trojans (HTs) poses a significant threat to cyberspace, and they may lead to catastrophic consequences for the national economy and personal privacy. To deal with HTs well, it is not enough to just detect whether they are included, like the existing studies. Same as malware, identifying the attack intentions of HTs, that is, analyzing the functions they implement, is of great scientific significance for the prevention and control of HTs. Based on the fined detection, for the first time, this article proposes a two-stage Graph Neural Network model for HTs' multifunctional classification, GNN4HT. In the first stage, GNN4HT localizes HTs, achieving a notable true positive rate (TPR) of 94.28% on the Trust-Hub dataset and maintaining high performance on the TRTC-IC dataset. GNN4HT further transforms the localization results into HT information graphs (HTIGs), representing the functional interaction graphs of HTs. In the second stage, the dataset is augmented through logical equivalence for training and HT functionalities are classified based on the extracted HTIG from the first stage. For the multifunctional classification of HTs, the correct classification rate reached as high as 80.95% at gate-level and 62.96% at register transfer level. This article marks a breakthrough in HT detection, and it is the first to address the multifunctional classification issue, holding significant practical importance and application prospects.
Keyword ：
Gate level Gate level golden free golden free Hardware Hardware hardware Trojan (HT) hardware Trojan (HT) HT information graph (HTIG) HT information graph (HTIG) HT location HT location HT multifunctional classification HT multifunctional classification Integrated circuit modeling Integrated circuit modeling Location awareness Location awareness Logic gates Logic gates register transfer level (RTL) register transfer level (RTL) Security Security Training Training Trojan horses Trojan horses
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| GB/T 7714 | Chen, Lihan , Dong, Chen , Wu, Qiaowen et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification [J]. | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) : 172-185 . |
| MLA | Chen, Lihan et al. "GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification" . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 44 . 1 (2025) : 172-185 . |
| APA | Chen, Lihan , Dong, Chen , Wu, Qiaowen , Liu, Ximeng , Guo, Xiaodong , Chen, Zhenyi et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) , 172-185 . |
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Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.
Keyword ：
attention mechanism attention mechanism Attention mechanisms Attention mechanisms convolutional neural network convolutional neural network Discrete wavelet transforms Discrete wavelet transforms Feature extraction Feature extraction feature fusion feature fusion Image edge detection Image edge detection Image manipulation localization Image manipulation localization Location awareness Location awareness Neural networks Neural networks Noise Noise Semantics Semantics Splicing Splicing Transformers Transformers
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| GB/T 7714 | Chen, Yun , Cheng, Hang , Wang, Haichou et al. EAN: Edge-Aware Network for Image Manipulation Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) : 1591-1601 . |
| MLA | Chen, Yun et al. "EAN: Edge-Aware Network for Image Manipulation Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 2 (2025) : 1591-1601 . |
| APA | Chen, Yun , Cheng, Hang , Wang, Haichou , Liu, Ximeng , Chen, Fei , Li, Fengyong et al. EAN: Edge-Aware Network for Image Manipulation Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) , 1591-1601 . |
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Data is the essential fuel for deep neural networks (DNNs), and its quality affects the practical performance of DNNs. In real-world training scenarios, the successful generalization performance of DNNs is severely challenged by noisy samples with incorrect labels. To combat noisy samples in image classification, numerous methods based on sample selection and semi-supervised learning (SSL) have been developed, where sample selection is used to provide the supervision signal for SSL, achieving great success in resisting noisy samples. Due to the necessary warm-up training on noisy datasets and the basic sample selection mechanism, DNNs are still confronted with the challenge of memorizing noisy samples. However, existing methods do not address the memorization of noisy samples by DNNs explicitly, which hinders the generalization performance of DNNs. To alleviate this issue, we present a new approach to combat noisy samples. First, we propose a memorized noise detection method to detect noisy samples that DNNs have already memorized during the training process. Next, we design a noise-excluded sample selection method and a noise-alleviated MixMatch to alleviate the memorization of DNNs to noisy samples. Finally, we integrate our approach with the established method DivideMix, proposing Modified-DivideMix. The experimental results on CIFAR-10, CIFAR-100, and Clothing1M demonstrate the effectiveness of our approach.
Keyword ：
Accuracy Accuracy Artificial neural networks Artificial neural networks Deep neural networks Deep neural networks Entropy Entropy Filtering algorithms Filtering algorithms Image classification Image classification image classification. label flipping attack image classification. label flipping attack Noise Noise Noise measurement Noise measurement noisy label learning noisy label learning Reviews Reviews sample selection sample selection Semisupervised learning Semisupervised learning Training Training
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| GB/T 7714 | Yuan, Shunjie , Li, Xinghua , Miao, Yinbin et al. Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 : 597-609 . |
| MLA | Yuan, Shunjie et al. "Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels" . | IEEE TRANSACTIONS ON MULTIMEDIA 27 (2025) : 597-609 . |
| APA | Yuan, Shunjie , Li, Xinghua , Miao, Yinbin , Zhang, Haiyan , Liu, Ximeng , Deng, Robert H. . Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels . | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 , 597-609 . |
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With the rapid growth of encrypted image data outsourced to cloud servers, achieving data confidentiality and searchability in cloud-assisted Internet of Things (IoT) environments has become increasingly feasible. However, achieving high efficiency and strong security simultaneously over large-scale encrypted image datasets remains a challenge. To address this, we propose a novel efficient and secure content-based image retrieval scheme in cloud-assisted IoT. Specifically, our scheme leverages lattice-based fully homomorphic encryption and homomorphic comparison techniques, utilizing Cheon-Kim-Kim-Song's batch processing and single-instruction-multiple-data capabilities. This approach significantly reduces the overhead of fully homomorphic computations, making the query process computational complexity independent of dataset size under certain conditions. Moreover, by integrating private information retrieval technology, the scheme enhances privacy by hiding access patterns of image data. Formal security analysis demonstrates that our scheme achieves indistinguishability against chosen-plaintext attack (IND-CPA), and extensive experiments based on real datasets confirm that our scheme is both practical and efficient for real-world applications.
Keyword ：
Cloud computing Cloud computing Content-based image retrieval (CBIR) Content-based image retrieval (CBIR) Data privacy Data privacy encrypted image data encrypted image data Feature extraction Feature extraction fully homomorphic encryption (HE) fully homomorphic encryption (HE) homomorphic comparison homomorphic comparison Homomorphic encryption Homomorphic encryption Image retrieval Image retrieval Indexes Indexes Internet of Things Internet of Things Nearest neighbor methods Nearest neighbor methods Privacy Privacy private information retrieval (PIR) private information retrieval (PIR) Security Security
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| GB/T 7714 | Chen, Lin , Yang, Yiwei , Yang, Li et al. Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) : 6001-6013 . |
| MLA | Chen, Lin et al. "Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things" . | IEEE INTERNET OF THINGS JOURNAL 12 . 5 (2025) : 6001-6013 . |
| APA | Chen, Lin , Yang, Yiwei , Yang, Li , Xu, Chao , Miao, Yinbin , Liu, Zhiquan et al. Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) , 6001-6013 . |
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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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