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Poisoning as a Post-Protection: Mitigating Membership Privacy Leakage From Gradient and Prediction of Federated Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Gaoyang Xu, Tianlong Yang, Yang Abdelmoniem, Ahmed M. Wang, Chen Liu, Jiangchuan Huazhong University of Science and Technology Hubei Key Laboratory of Internet of Intelligence School of Electronic Information and Communications Wuhan430074 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Ministry of Education Key Laboratory of Intelligent Sensing System and Security China Hubei University School of Artificial Intelligence Wuhan430062 China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to train a unified model without sharing their private data. However, recent works demonstrate that FL models are vulnerable to membership inference attacks (MIAs), which can infer whether a data sample was used to train a given FL model. Existing countermeasures either require far-reaching modifications of FL training process or enforce extra processing in prediction phase, yielding them unlikely to be applied well in practice. In this paper, we design a post-protection mechanism, dubbed P2-Protection, which degrades the inference performance of MIAs by simultaneously poisoning the prediction and gradient of the target FL model to reduce the privacy leakage of training data while keeping the model prediction accuracy. P2-Protection only involves one additional training round to embed the poisoned prediction and gradient into the target FL model, without requiring model retraining or training process modification. We evaluate P2-Protection and compare it with two state-of-the-art defenses against three MIAs on five realistic datasets. Experimental results show that P2-Protection outperforms the existing defenses by offering limited implement overhead and improved utility-privacy trade-off. © 2004-2012 IEEE.

关键词： Federated learning

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A novel biologically plausible spiking convolutional capsule network with optimized batch normalization for EEG-based emotion recognition

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Expert systems with Applications 2025年 288卷

作者： Kun Chen Zihao Yang Mincheng Cai Quan Liu Qingsong Ai Li Ma School of Information Engineering Wuhan University of Technology Wuhan 430070 China Key Laboratory of Intelligent Sensing System and Security(Hubei University) Ministry of Education Wuhan 430062 Hubei China Key Laboratory of Language Cognitive Science of Ministry of Education Beijing Language and Culture University Beijing 100083 China

Emotion recognition based on Electroencephalogram (EEG) is currently a hot topic in human-computer interaction, as EEG can more accurately reflect the characteristics of emotion. In recent years, EEG emotion recognition based on deep learning has achieved significant progress, and particularly methods combined with a capsule network (CapsNet) have outstanding performance. However, as the complexity of models continues to increase, resource consumption has also escalated. In this context, spiking neural networks (SNNs), known for their energy efficiency and greater biological plausibility, have attracted attentions of numerous researchers. Nevertheless, convolutional neural networks (CNNs) -based methods are not effective in SNN, and there remains a considerable gap compared with artificial neural networks (ANNs). This paper proposes a novel method combining the high-performing capsule networks with SNNs, named the spiking convolutional capsule network (SCCapsNet) for EEG-based emotion recognition tasks. To our knowledge, this is the first attempt to introduce capsule networks into SNNs for EEG emotion recognition. Furthermore, the spike-timing-dependent plasticity (STDP) routing algorithm is improved to sensitively capture temporal sequence information of EEG signals to enhance biological plausibility of SCCapsNet. In addition, a novel batch normalization (BN) layer incorporating the membrane potential decay time constant (tau-BN) is suggested to address the issue of neuron death caused by reduction in spike firing rate due to the L 2 norm. We provide a theoretical explanation of the role of the BN layer in regulating spike firing rates. Finally, the performance of SCCapsNet is validated on two public datasets. As for DEAP dataset, SCCapsNet achieved recognition accuracies of 97.01 %, 96.84 %, and 96.73 % for valence, arousal, and dominance dimensions, respectively. The accuracies are 89.82 %, 93.69 %, and 93.90 % on the same dimensions for DREAMER dataset. A recogn

关键词： Convolutional neural networks

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Dual topology and versatile Rashba-split surface state configurations in 2M−WS2 and 2M−WSe2

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Physical Review B 2025年第11期111卷 115151-115151页

作者： Lixuan Xu Yuqiang Fang Fuqiang Huang Shihao Zhang Shiheng Liang Yulin Chen Lexian Yang Zhongkai Liu Nan Xu Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Physics and Electronics Hunan University Changsha 410082 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China ShanghaiTech Laboratory for Topological Physics Shanghai 201210 China Department of Physics University of Oxford Oxford OX1 3PU United Kingdom State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Institute for Advanced Studies (IAS) Wuhan University Wuhan 430072 China Wuhan Institute of Quantum Technology Wuhan 430206 China

The 2M-phase transition metal dichalcogenides have recently attracted intensive research interest due to their rich topological and superconducting phase diagrams. Apart from the topological surface states of 2M−WS2 near Γ that originated from the strong topological order, using angle-resolved photoemission spectroscopy, we discover additional Rashba-split states on the surfaces of both 2M−WS2 and 2M−WSe2, which extend in large momentum-energy regions. First-principles calculations well reproduce these states and attribute them to the weak topological orders. The calculations further indicate that the surface state connecting configurations are tunable under moderate pressure, suggesting that 2M−WS2 and WSe2 are promising platforms to study topological phase transition and explore topological superconductivity.

关键词： Photoacoustic spectroscopy

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Distributed privacy-preserving keyword querying for integrated data in IoT networks via function secret sharing

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Information Fusion 2025年 123卷

作者： Wei Shao Lianhai Wang Chunfu Jia Qizheng Wang Jinpeng Wang Shujiang Xu Shuhui Zhang Mingyue Li Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 Shandong China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin 300350 China School of Cyber Sciences Hebei University Baoding Hebei China

The growing adoption of IoT applications underscores the need for advanced data fusion and information acquisition techniques, driving demand for secure, privacy-preserving querying of integrated IoT data. Existing schemes like searchable encryption are practical but leak access patterns, while leakage-free methods using Oblivious RAM or cryptographic techniques incur significant resource overhead. In this paper, we propose PQBL, a framework for privacy-preserving, trusted data integration and search, leveraging distributed trust against malicious attackers. Our query scheme combines function secret sharing and blockchain to enable efficient, privacy-preserving searches on encrypted IoT data. To improve search efficiency, we introduce a compressed RAMBO Bloom Filter for keyword trapdoors. Formal security analysis shows that PQBL leaks no search patterns and is secure against Privacy under Selective Chosen-Plaintext Attacks. Extensive experiments on the PQBL prototype validate its effectiveness and efficiency.

关键词： Privacy by design

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Adaptive offloading scheme of fine-grained tasks based on reinforcement learning in multi-access edge computing

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Jie Li Ge Chen Kansong Chen School of Computer Science Hubei University Wuhan 430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 China Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China

In the context of multi-access edge computing, the constraints on device resources and energy pose a challenge in meeting the requirements of delay-sensitive and high-resource demand tasks. In order to address this issue, this paper puts forth an adaptive offloading decision based on fine-grained task sequencing (AOD-FTS). To enhance task execution efficiency and resource utilization, we design scheduling optimization for subtasks algorithm within the fine-grained task model to optimize the execution order of subtasks and improve subtask parallelism. Additionally, to reduce task delay and energy consumption, we propose an adaptive multi-objective reinforcement learning offloading algorithm based on ordered subtasks, formulated through multi-objective Markov decision processes. The scheme dynamically adjusts optimization preferences according to environmental changes and task completion states to make offloading decisions. Simulation results demonstrate that compared with two benchmark algorithms, the proposed AOD-FTS scheme achieves: (1) higher task completion rates, (2) improved resource utilization efficiency, (3) 81.66 % and 51.53 % reductions in delay respectively, and (4) 39.72 % and 71.36 % reductions in energy consumption respectively.

关键词： Reinforcement learning

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Adaptive Perception for Unified Visual Multi-modal Object Tracking

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hu, Xiantao Zhong, Bineng Liang, Qihua Shi, Liangtao Mo, Zhiyi Tai, Ying Yang, Jian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Guilin541004 China Guangxi Normal University Security Guilin541004 China Guangxi Normal University Intelligent Technology Guilin541004 China Guangxi Normal University Ministry of Education Guilin541004 China Wuzhou University School of Data Science Wuzhou543002 China Wuzhou University Software Engineering Wuzhou543002 China Nanjing University State Key Laboratory for Novel Software Technology Suzhou215163 China Nanjing University of Science and Technology School of Computer Science Nanjing210094 China Nanjing University of Science and Technology Engineering Nanjing210094 China

Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex scenarios, making it challenging to fully perceive the advantages of multi-modal. As a result, a unified parameter model often underperforms in various multi-modal tracking tasks. To address this issue, we propose APTrack, a novel unified tracker designed for multi-modal adaptive perception. Unlike previous methods, APTrack explores a unified representation through an equal modeling strategy. This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks. Moreover, our tracker integrates an adaptive modality interaction (AMI) module that efficiently bridges cross-modality interactions by generating learnable tokens. Experiments conducted on five diverse multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022) demonstrate that APTrack not only surpasses existing state-of-the-art unified multi-modal trackers but also outperforms trackers designed for specific multi-modal tasks. © 2020 IEEE.

关键词： Multicarrier modulation

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Link prediction for attribute and structure learning based on attention mechanism

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Applied Soft Computing 2025年 179卷

作者： Renjuan Nie Guoyin Wang Qun Liu Chengxin Peng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing 401331 China

The link prediction problem within complex network analysis focus on accurately inferring future relationships between unconnected node pairs in networks. In real-world scenarios, many relationships can find effective expression through network representation learning, making link prediction pivotal in solving various practical problems. However, existing link prediction methods often exhibit an over-reliance on network structure information or struggle to effectively integrate network structure with node attribute information. Moreover, the current limitations of GPU memory pose challenges for deploying graph neural network (GNN) methods at scale. To address these challenges, this paper introduces attribute and structure learning based on attention mechanism(ASLAM), a novel link prediction framework that amalgamates graph neural networks with attention mechanisms. ASLAM adeptly extracts and integrates both node attribute and structural information, constructing semantic and structural node representations through attention mechanisms and facilitating predictions via adaptive merging. Additionally, to mitigate GPU memory consumption, ASLAM innovatively incorporates the subgraph history embedding method, ensuring stable performance with either constant or linear increases in memory consumption. Experimental results show ASLAM’s superiority over baseline methods across nine real datasets in link prediction tasks. The AUC and AP metrics witness enhancements ranging from 0.11% to 3.03% and 0.15% to 3.32%, respectively, underscoring its promise and applicability.

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Robust low-rank tensor factorization by cyclic weighted median

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Science China(Information Sciences) 2015年第5期58卷 145-155页

作者： MENG DeYu ZHANG Biao XU ZongBen ZHANG Lei GAO ChenQiang Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Department of Computing Hong Kong Polytechnic University Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.

关键词： tensor factorization low-rank weighted median filter tensorface

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Modeling and analysis of the dynamic mechanism of rumor propagation considering the process of rumor refuting

Nonlinear Science

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Nonlinear Science 2025年 3卷

作者： Xutao Li Junshan Pan Yong Hu Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education School of Cyber Science and Technology Hubei University Wuhan 430062 Hubei China School of Physics Huazhong University of Science and Technology Wuhan 430074 Hubei China

The boost of rumor spreading by the Internet has caused a series of social problems. Refuting the rumor timely can alleviate the harm caused by rumor dissemination. To investigate the propagation mechanism of rumor affected by rumor refuting on online social platforms, we propose the Susceptible-Infective-Remover-Debunker-Remover (SIRDR) model to describe the propagation dynamics of rumor and rumor refutation. To conform closely to the actual scenarios of rumor dissemination, we consider a refined division of the population involved in propagation and introduce an information exposure attractiveness index and forgetting mechanisms to characterize individual spreading behavior. We then investigate the extent to which the suppression of rumor dissemination by refuting rumors at different spreading stages. The proposed model is validated by the analysis of real datasets collected from Sina Weibo and Twitter. The R-squared values of data 1 are 0.947 and 0.985, the R-squared values of data 2 are 0.993 and 0.976, while the R-squared values of data 3 are 0.948 and 0.956, indicating that our model is very consistent with the real rumor propagation process. Especially, we find out that the suppression effect of rumor refutation on rumor propagation varies greatly with its release time. Our work may offer theoretical support for rumors governance in cyberspace.

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