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Robust privacy-preserving aggregation against poisoning attacks for secure distributed data fusion

information Fusion 2025年 122卷

作者： Huang, Chao Yao, Yanqing Zhang, Xiaojun Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyber Science and Technology Beihang University Beijing100191 China State Key Laboratory of Cryptology Beijing100878 China State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

Privacy-preserving data aggregation could be well applied in federated learning, enabling an aggregator to learn a specified fusion statistics over private data held by clients. Besides, robustness is a critical requirement in federated learning, since a malicious client is able to readily launch poisoning attacks by submitting artificial and malformed model updates to central server. To this end, we present a robust privacy-preserving data aggregation protocol based on distributed trust model, which achieves privacy protection by three-party computation based on replicated secret sharing with honest-majority. The protocol also achieves robustness by securely computing an input validation strategy called norm bounding, including ∞-norm and 2-norm bounding, which has been proven effective to defend against poisoning attacks. Following the best practice of hybrid protocol design, we exploit both Boolean sharing and arithmetic sharing to efficiently enforce ∞ and 2-norm bounding respectively. Additionally, we propose a novel share conversion protocol converting Boolean shares into arithmetic ones, which is of independent interest and could be used in other protocols. We provide security analysis of the protocol based on standard simulation paradigm and modular composition theorem, reaching the conclusion that presented protocol achieves secure aggregation functionality with norm bounding with computational security in the presence of one static semi-honest server. Comprehensive efficiency analysis and empirical experiments demonstrate its superiority compared with related protocols. © 2025 Elsevier B.V.

关键词： Federated learning

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Privacy-Preserving IoT Image Transmission: Multi-Stage SVD Data Embedding and Heatmap Alignment

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IEEE Internet of Things Journal 2025年

作者： Du, Kaixin Ma, Bin Yang, Meihong Wang, Xiaoyu Li, Xiaolong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250101 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under *** method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions. © 2014 IEEE.

关键词： Discrete cosine transforms

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Energy-and-Spectrum-Efficient Trade-off Oriented Multi-Domains Resource Management Approach in RIS-Aided IABN Using NOMA With Imperfect SIC

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Ma, Zhongyu Li, Jing Han, Guangjie Wang, Yajing Liu, Zhirong Hao, Zhanjun Guo, Qun Northwest Normal University College of Computer Science and Engineering Lanzhou730070 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education 213000 China Northwestern Polytechnical University School of Electronic Information Xi'an710129 China Lanzhou University of Technology College of Electrical and Information Engineering Lanzhou730050 China

Although reconfigurable intelligent surface (RIS)-assisted millimeter-wave integrated access and backhaul networks (mmWave IABN) offer a cost-effective solution for 6G systems, they face difficulties in meeting the spectral efficiency (SE) and energy efficiency (EE) requirements due to environmental uncertainties and interference. This paper focuses on the application of RIS-aided mmWave IABN and proposes an energy-and-spectrum-efficient trade-off oriented multi-domains resource management (ETOM-RM) scheme to address these challenges by optimizing the trade-off between SE and EE. The scheme uses non-orthogonal multiple access (NOMA) technology combined with imperfect successive interference cancellation (SIC), and it decomposes the optimization problem into two sub-problems: user transmission power and RIS phase-shift control, as well as bandwidth partition between access and backhaul. Simulation results validate the ETOM-RM scheme's effectiveness in enhancing SE and optimizing the SE-EE trade-off. Compared to the OMA-RMM and RPS-RMM schemes, it demonstrates superior performance. Additionally, the observed trade-off between SE and EE underscores the scheme's robustness across diverse scenarios, which provides concrete guidelines for advancing environmentally sustainable wireless communication technologies. © 2017 IEEE.

关键词： Resource allocation

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

引用

arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

引用

IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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ChainPIM: A ReRAM-Based Processing-in-Memory Accelerator for HGNNs via Chain Structure

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xiao, Wenjing Wang, Jianyu Chen, Dan Shi, Chenglong Ling, Xin Chen, Min Wu, Thomas School of Computer Electronics and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China School of Computing National University of Singapore 119077 Singapore School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Pazhou Laboratory Guangzhou510330 China School of Electrical Engineering Guangxi University Nanning530004 China

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant advantages of capturing powerful structural and semantic information in heterogeneous graphs. Different from homogeneous graph neural networks directly aggregating information based on neighbors, HGNNs aggregate information based on complex metapaths. ReRAM-based processing-in-memory (PIM) architecture can reduce data movement and compute matrix-vector multiplication (MVM) in analog. It can be well used to accelerate HGNNs. However, the complex metapath-based aggregation of HGNNs makes it challenging to efficiently utilize the parallelism of ReRAM and vertices data reuse. To this end, we propose ChainPIM, the first ReRAM-based processing-in-memory accelerator for HGNNs featuring high computing parallelism and vertices data reuse. Specifically, we introduce R-Chain, which is based on a chain structure to build related metapath instances together. We can efficiently reuse vertices through R-Chain and process different R-Chains in parallel. Then, we further design an efficient storage format for storing R-Chains, which reduces a lot of repeated vertices storage. Finally, a specialized ReRAM-based architecture is developed to pipeline different types of aggregations in HGNNs, fully exploiting the huge potential of multi-level parallelism in HGNNs. Our experiments show that ChainPIM achieves an average memory space reduction of 47.86% and performance improvement by 128.29× compared to NVIDIA Tesla V100 GPU. © 1982-2012 IEEE.

关键词： Random access storage

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Self-Attention Policy Optimization for Task Offloading and Resource Allocation in Low-Carbon Agricultural Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Yi Zeng, Jisong Wei, Yanting Chen, Miaojiang Xiao, Wenjing Yang, Yang Liu, Zhiquan Farouk, Ahmed Song, Houbing Herbert Guangxi University School of Computer Electronics and Information China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Jinan University College of Cyber Security Guangzhou510632 China Faculty of Computers and Artificial Intelligence Egypt Department of Information Systems BaltimoreMD21250 United States

In recent years, the widespread use of edge agricultural consumer electronics has greatly contributed to the level of intelligence in agricultural production, bringing higher efficiency and quality. However, offloading all tasks to the cloud incurs significant latency and resource waste, while relying solely on edge computing fails to meet the computational demands of the entire system. To solve the above problems, we introduce the device-edge-cloud (DEC) three-layer architecture, where agri-consumer electronics devices can partially offload tasks to the edge, and the edge can partially offload tasks to the cloud, i.e., agri-consumer electronics can realize device-edge-cloud collaborative computation. Second, we model the joint computation offloading and resource allocation optimization problem as a non-convex optimization and propose a novel Self-Attention Policy Optimization (SAPO) algorithm to solve it. Experiments show that the joint optimization performance of the proposed SAPO exceeds the baseline, and it is suitable for many different models. Compared with fully connected networks, it has better convergence and robustness, with a convergence speed 50% faster than the fully connected networks. The proposed SAPO algorithm has good scalability and adaptability, and has the potential to be extended to smart agricultural computing scenarios with non-convex optimization. © 1975-2011 IEEE.

关键词： Computation offloading

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Partially Hidden Policy Attribute-Based Multi-keyword Searchable Encryption with Verification and Revocation

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Liqing Xu, Shiguo Jin, Chunhua Zhang, Hao Weng, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huai'an223003 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis. © 2002-2012 IEEE.

关键词： Differential privacy

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STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

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