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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

IEEE Transactions on Information Forensics and security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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A Provably Secure Certificateless Signature Scheme With Anonymity for Healthcare IIoT

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IEEE Internet of Things Journal 2025年

作者： Qiao, Zirui Xu, Ran Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Xi’an University of Posts and Telecommunications School of Cyberspace Security Xi’an China Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Shaanxi Normal University School of Computer Science Xi’an China City University of Macau Faculty of Data Science Macau China Hubei University of Technology School of Computer Wuhan China

The industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges such as insecure communication environments. The certificateless signature (CLS) scheme can ensure the integrity and validity of data and provide secure identity authentication for the IIoT, and several pairing-free CLS schemes have been proposed in recent years. However, we find they are vulnerable to the signature forgery attack of malicious key generation centers by safety analysis, which does not realize the security they have claimed. To solve this problem, we show an improved CLS scheme that achieves anonymity and formally proves its security under the hardness of the discrete logarithm problem in the random oracle. Comprehensive performance analysis and comparison show that our scheme has less communication and computation costs with higher security, and our construction is suitable for scenarios with limited resources. Finally, we apply this scheme to construct a mutual identity authentication protocol in the healthcare IIoT environment. 2014 IEEE.

关键词： Anonymity

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Robust Quantum Feature Selection With Sparse Optimization Circuit

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

作者： Li, Jiaye Song, Jiagang Shi, Jinjing Xu, Hang Yu, Hao Chen, Gang Zhang, Shichao Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security China Central South University School of Computer Science and Engineering Changsha410083 China Central South University School of Electronic Information Changsha410083 China Xiangtan University School of Computer Science and School of Cyberspace Science Hunan Xiangtan411105 China Guangxi Normal University Guangxi Key Lab of Multisource Information Mining and Security Guilin541004 China

High-dimensional data has long been a notoriously challenging issue. Existing quantum dimension reduction technology primarily focuses on quantum principal component analysis. However, there are only a few studies on quantum feature selection (QFS) algorithms, and these algorithms are often not robust. Additionally, there are limited quantum circuits specifically designed for feature selection, and they still cannot address the objective function based on sparse learning. To address these issues, this paper proposes a robust QFS algorithm by designing a novel sparse optimization circuit. Specifically, we first apply sparse regularization and least squares loss to construct the proposed objective function. Then, six types of quantum registers and their initial states are prepared. Furthermore, quantum techniques such as quantum phase estimation and controlled rotation are employed to construct a sparse optimization circuit, which is used to obtain the final quantum state of the feature selection ***, a series of experiments are conducted to verify the accuracy of the feature selection and the robustness of the proposed algorithm. © 1982-2012 IEEE.

关键词： Quantum electronics

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

引用

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

作者： Shenhai Zheng Congyu Li Sian Wen Xi Gao Lei Yu College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L 2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP.

关键词： Training Adaptation models Federated learning Collaboration Signal processing Servers Speech processing Optimization Image classification Biomedical imaging

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Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

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IEEE Network 2025年

作者： Li, Xuzeng Zhang, Tao Wang, Jian Han, Zhen Liu, Jiqiang Kang, Jiawen Niyato, Dusit Jamalipour, Abbas Beijing Jiaotong University School of Cyberspace Science and Technology China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Guangdong University of Technology Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore University of Sydney SydneyNSW2006 Australia

Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. In order to improve the perception ability of DRL on the network, some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. However, as networks continue to evolve and become increasingly complex, existing GNN-DRL methods still face challenges in terms of scalability and robustness. Moreover, these methods are inadequate for addressing network security issues. From the perspective of security and robustness, this paper explores the solution of combining GNNs with DRL to build a resilient network. This article starts with a brief tutorial of GNNs and DRL, and introduces their existing applications in networks. Furthermore, we introduce the network security methods that can be strengthened by GNN-DRL approaches. Then, we designed a framework based on GNN-DRL to defend against attacks and enhance network resilience. Additionally, we conduct a case study using an encrypted traffic dataset collected from real IoT environments, and the results demonstrated the effectiveness and superiority of our framework. Finally, we highlight key open challenges and opportunities for enhancing network resilience with GNN-DRL. © 2025 IEEE. All rights reserved,

关键词： Deep reinforcement learning

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Dual consistent constraint via disentangled consistency and complementarity for multi-view clustering

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Neurocomputing 2025年 641卷

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025

关键词： Contrastive Learning

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Constructing Three-way Decision with Fuzzy Granular-ball Rough Sets Based on Uncertainty Invariance

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IEEE Transactions on Fuzzy Systems 2025年

作者： Yang, Jie Liu, Zhuangzhuang Wang, Guoyin Zhang, Qinghua Xia, Shuyin Wu, Di Liu, Yanmin Zunyi Normal University School of Physics and Electronic Science Zunyi563002 China Jiangsu University of Science and Technology School of Computer Zhenjiang212100 China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China Zunyi Normal University School of Mathematics Zunyi563002 China

Granular-ball computing (GBC) proposed by Xia adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility. Moreover, GBC greatly improves the efficiency by replacing point input with granular-ball. However, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. In this paper, we introduce three-way decision (3WD) into GBC to construct a novel three-way decision with fuzzy granular-ball rough sets (3WD-FGBRS) from the perspective of uncertainty. This helps to construct reasonable multi-granularity spaces for handling complex decision problems with uncertainty. Firstly, 3WD-FGBRS is constructed in a data-driven method based on fuzziness, which avoids the subjective definition of certain risk parameters when calculating the threshold pairs. Based on 3WD-FGBRS, we further propose a sequential three-way decision with fuzzy granular-ball rough sets (S3WD-FGBRS) and analyze the fuzziness loss of multilevel decision result in S3WD-FGBRS. Then, the optimal granular-ball space selection mechanism of S3WD-FGBRS is introduced by combining fuzziness and granular-ball space distance. Finally, extensive comparative experiments are conducted with 3 state-of-the-art GB-based classifiers and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our models almost outperform other comparison methods in terms of effectiveness, efficiency and robustness. © 1993-2012 IEEE.

关键词： Granular computing

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A Survey on Federated Long-Tailed Learning

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Jisuanji Xuebao/Chinese Journal of Computers 2025年第4期48卷 779-807页

作者： Zhou, Yi-Zhi Wang, Jun-Xiao Xie, Xin Wang, Peng-Fei Jia, Xi-Bei Qi, Heng Qin, Yu-Chen School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence（Dalian University of Technology） Ministry of Education Liaoning Dalian116024 China School of Cyberspace Security Guangzhou University Guangzhou510006 China College of Intelligence and Computing Tianjin University Tianjin300072 China Shenzhen Audaque Data Technology Co. Ltd. Guangdong Shenzhen518057 China

Federated learning, a machine learning method built on distributed training, effectively navigates the problem of data privacy leakage among users that joint modeling introduces. This effectiveness has led to the widespread use of federated learning-based methods in many fields. Yet, in practical applications of federated learning, the combined existence of statistical heterogeneity and long-tailed distribution presents a considerable hurdle, dramatically affecting the model's performance. Consequently, a principal objective of contemporary research is the mastery of the long-tailed challenge in a manner that maintains data privacy. Most of the existing researches on long-tailed learning and statistical heterogeneity are not viable in a federated learning setting. Furthermore, there is a paucity of detailed and comprehensive overviews of the cutting-edge methods in this area. This paper presents a comprehensive review of the research accomplishments in federated long-tailed learning. To begin with, the architecture of federated learning is briefly explained, followed by an introduction to core concepts and definitions concerning statistical heterogeneity, long-tailed learning, and federated long-tailed learning. The main challenges of federated long-tailed learning are mentioned as well, including differences in data distribution, imbalance in categories, and inconsistent updates between clients during training. Basing on different optimization methods, the algorithms for federated long-tailed learning are classified into two major categories: Model Component Improvement and Algorithm-based Optimization. Model Component Improvement methods include virtual feature re-training, classifier calibration, and personalized federal learning. Algorithm-based Optimization methods include cost-sensitive learning, knowledge distillation, client selection, and balance enhancement. Each algorithm's implementation details, as well as its advantages and disadvantages, are thoroughl

关键词： Federated learning

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Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Li, Xuzeng Zhang, Tao Wang, Jian Han, Zhen Liu, Jiqiang Kang, Jiawen Niyato, Dusit Jamalipour, Abbas The School of Cyberspace Science and Technology Beijing Jiaotong University China The Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China The Technology School of Automation Guangdong University of Technology China The College of Computing and Data Science Nanyang Technological University Singapore The University of Sydney SydneyNSW2006 Australia

关键词： Deep reinforcement learning

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