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International Conference on Smart Business and technology, ICSBT 2024

作者： Acim, Btissam Kharmoum, Nassim Lagmiri, Souad Najoua Ziti, Soumia Department of Computer Science Intelligent Processing & Security of Systems Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco IT Networking Security and Management Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Intelligence (AI) does expand possibilities and give an opportunity to achieve terrific outcomes and has innumerable potential uses. This systematic literature review focuses on the role of generative AI in the improvement of deepfake detection technologies, providing a comparison between different methods and assessing their effectiveness. By analyzing recent studies, it depicts the techniques employed most frequently in deepfake detection, such as the ones concentrating on the intricacies between the visual and auditory elements, the temporal domains, as well as any minor signs of alteration. Due to the fact that generative AI is rapidly improving, the methods that are used to detect its use are often inadequate, which calls for more studies on the subject matter. Besides, by combining audio and visual elements when performing the analysis, calling it as a method that can be useful in focusing the detection growth potential with diverse and unstructured data. Also, this study evaluates the advantages and limitations of the techniques existing today and demonstrates their relative effectiveness in controlled and real conditions. Clarifying that measures aimed at detecting AI generated content should be preoccupied with a technique that is able to keep pace with changes in generative AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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C-privacy:A social relationship-driven image customization sharing method in cyber-physical networks

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Digital Communications and Networks 2025年第2期11卷 563-573页

作者： Dapeng Wu Jian Liu Yangliang Wan Zhigang Yang Ruyan Wang Xinqi Lin School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Aerospace Tianmu Satellite Technology Company Limited Chongqing401332China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China College of Computer and Cyber Security Fujian Normal UniversityFujian350117China The Advanced Network and Intelligent Connection Technology Key Laboratory Chongqing400065China Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China

Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on ***,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)*** image sharing methods lack fine-grained image sharing policies and cannot protect user *** address this issue,we propose a social relationship-driven privacy customization protection model for publishers and *** construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest *** protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be *** proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.

关键词： Cyber-physical networks Customized privacy Face-swapping Heterogeneous information network Deep fakes

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RobustPFL: Robust Personalized Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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Dynamic Radio Map Construction With Minimal Manual Intervention: A State Space Model-Based Approach With Imitation Learning

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IEEE Transactions on Big Data 2024年

作者： Zhu, Xiaoqiang Qiu, Tie Qu, Wenyu Zhou, Xiaobo Shi, Tuo Xu, Tianyi Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China

Fingerprint localization methods typically require a substantial amount of manual effort to collect fingerprint data from various scenarios to construct an accurate radio map. While some existing research has attempted to use path planning strategies to save on labor costs, these approaches often suffer from being time-consuming and prone to locally optimal solutions. To address these shortcomings, our paper proposes a novel approach that utilizes imitation learning to construct and update a highly accurate radio map with minimal manual intervention in dynamic environments. Specifically, we employ a multivariate Gaussian process model to fit a rough standby fingerprint database with only a few pilot data points. We then utilize a state space model to calculate the variation range of the pilot data, which forms the CSI error band used to filter the rough radio map. Imitation learning and a confidence coefficient are utilized to predict and calibrate the global CSI data distribution. And we utilize the K-nearest neighbor algorithm to achieve the real-time localization function. Experimental results show that our proposed algorithm outperforms several state-of-the-art approaches in most test cases, exhibiting low computation complexity, lower localization error, and saving 73.3% of the manual workload. © 2015 IEEE.

关键词： Wages

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A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

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Journal of Cyber security 2022年第1期4卷 29-39页

作者： Meng-Wei Zhang Xiu-Bo Chen Haseeb Ahmad Gang Xu Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile University Faisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing100044China

Blockchain has a profound impact on all areas of society by virtue of its immutability,decentralization and other ***,blockchain faces the problem of data privacy leakage during the application process,and the rapid development of quantum computing also brings the threat of quantum attack to *** this paper,we propose a lattice-based certificateless fully homomorphic encryption(LCFHE)algorithm based on approximate eigenvector *** we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme,which is composed of the private key together with the secret value selected by the user,thus effectively avoiding the problems of certificate management and key ***,we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm,which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount,effectively protecting the transaction privacy of users and having the ability to resist quantum ***,we analyze the correctness and security of LCFHE algorithm,and the security of the algorithm reduces to the hardness of learning with errors(LWE)hypothesis.

关键词： Blockchain homomorphic encryption lattice privacy protection

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Towards Privacy-Preserving Cloud Storage:A Blockchain Approach

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computers, Materials & Continua 2021年第12期69卷 2903-2916页

作者： Jia-Shun Zhang Gang Xu Xiu-Bo Chen Haseeb Ahmad Xin Liu Wen Liu Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Inner Mongolia University of Science&Technology School of Information EngineeringBaotou014010China State Key Laboratory of Media Convergence and Communication Communication University of China School of Computer Science and Cybersecurity Communication University of China Key Laboratory of Convergent Media and Intelligent Technology Communication University of China Ministry of EducationBeijing102204China

With the rapid development of cloud computing technology,cloud services have now become a new business model for information *** cloud server provides the IT resources required by customers in a selfservice manner through the network,realizing business expansion and rapid ***,due to the insufficient protection of data privacy,the problem of data privacy leakage in cloud storage is threatening cloud *** address the problem,we propose BC-PECK,a data protection scheme based on blockchain and public key searchable ***,all the data is protected by the encryption *** privacy data is encrypted and stored in a cloud server,while the ciphertext index is established by a public key searchable encryption scheme and stored on the ***,based on the characteristics of trusted execution of smart contract technology,a control mechanism for data accessing and sharing is *** transaction is automatically recorded on the blockchain,which is fairer under the premise of ensuring the privacy and security of the data sharing ***,we analyzed the security and fairness of the current *** the comparison with similar schemes,we have shown the advantages of the proposed scheme.

关键词： Blockchain privacy protection smart contract public encryption keyword search

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A survey on security attacks and defense techniques for Connected and Autonomous Vehicles

arXiv

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arXiv 2020年

作者： Pham, Minh Xiong, Kaiqi Department of Computer Science and Engineering University of South Florida United States Intelligent Computer Networking and Security Laboratory University of South Florida United States

Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and to infrastructures, forming Connected and Autonomous Vehicles (CAVs). CAVs will help humans achieve safe, efficient, and autonomous transportation systems. However, CAVs will face significant security challenges because many of their components are vulnerable to attacks, and a successful attack on a CAV may have significant impacts on other CAVs and infrastructures due to their communications. In this paper, we conduct a survey on 184 papers from 2000 to 2020 to understand state-of-the-art CAV attacks and defense techniques. This survey first presents a comprehensive overview of security attacks and their corresponding countermeasures on CAVs. We then discuss the details of attack models based on the targeted CAV components of attacks, access requirements, and attack motives. Finally, we identify some current research challenges and trends from the perspectives of both academic research and industrial development. Based on our studies of academic literature and industrial publications, we have not found any strong connection between academic research and industry’s implementation on CAV-related security issues. While efforts from CAV manufacturers to secure CAVs have been reported, there is no evidence to show that CAVs on the market have the ability to defend against some novel attack models that the research community has recently found. This survey may give researchers and engineers a better understanding of the current status and trend of CAV security for CAV future improvement. Copyright © 2020, The Authors. All rights reserved.

关键词： intelligent systems

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A two-phase approach for locally differentially private fuzzy co-clustering

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Information Sciences 2025年 718卷

作者： Zhang, Pengfei An, Jianlong Cheng, Xiang Zhu, Youwen Zhang, Ji Zhang, Zhikun State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining No. 168 Shungeng Road Tianjiaan District Huainan232001 China School of Computer Science and Engineering Anhui University of Science and Technology No. 168 Shungeng Road Tianjiaan District Huainan232001 China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics No. 237 Longquan Road Yunnan Kunming650221 China No. 3501 Daxue Road Shandong Jinan255020 China Key Laboratory of Equipment Data Security and Guarantee Technology Guilin University of Electronic Technology No. 1 Jinji Road Guangxi Guilin541010 China State Key Laboratory of Networking and Switching Technology No. 10 Xitucheng Road Haidian District Beijing100876 China Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China College of Computer and Data Science Nanjing University of Aeronautics and Astronautics No. 29 Yudao Street Qinhuai District Jiangsu Nanjing210016 China School of Mathematics Physics and Computing University of Southern Queensland West Street ToowoombaQLD4350 Australia College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Xihu District Hangzhou310027 China

Clustering analysis is important for providing personalized services across various user experience optimization. As an advanced clustering technique, Fuzzy Co-Clustering (FCC) excels at handling high-dimensional data by simultaneously grouping both samples and attributes while allowing objects to belong to multiple clusters. To alleviate the potential privacy risks posed by directly using raw data, researchers often employ local differential privacy (LDP) techniques for protection. However, current clustering studies under LDP either directly perform clustering on the high-dimensional data, which necessitates splitting the privacy budget heavily, leading to excessive noise injection, or fail to adequately address the impact of Laplacian noise on both objective function modeling and distance metric selection, resulting in reduced clustering accuracy. To address these limitations, we present a two-phase approach SONY, which allocates part of the privacy budget to evaluate attribute importance and perform dimensionality reduction accordingly, and then utilizes the remaining budget to conduct noise-aware clustering on the pruned data. Our key technical contributions include: (1) a Noise-aware Similarity Calculation method (NSC) that replaces traditional Euclidean distance with a specialized metric which accounts for the statistical properties of Laplacian noise;(2) an importance-driven dimension reduction and budget allocation method (IDRBA) which optimizes both attribute selection and privacy budget allocation;and (3) an outlier-aware objective function calculation method (OOC) which enhances robustness against potential noise-induced outliers. We provide theoretical guarantees for SONY's convergence, privacy preservation, utility bounds, and computational complexity. Experimental evaluations conducted on two real-world datasets and one synthetic dataset, demonstrate that SONY outperforms the state-of-the-arts by at least 14% in F-Measure and 10% in Entropy while main

关键词： Data privacy

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Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computing

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Digital Communications and Networks 2025年

作者： Dapeng Wu Sijun Wu Yaping Cui Ailing Zhong Tong Tang Ruyan Wang Xinqi Lin School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing 400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing 400065 China College of Computer and Cyber Security Fujian Normal University Fuzhou 350117 China

Vehicular Edge Computing (VEC) enhances the quality of user services by deploying wealth of resources near vehicles. However, due to highly dynamic and complex nature of vehicular networks, centralized decision-making for resource allocation proves inadequate within VECs. Conversely, allocating resources via distributed decision-making consumes vehicular resources. To improve the quality of user service, we formulate a problem of latency minimization, further subdividing this problem into two subproblems to be solved through distributed decision-making. To mitigate the resource consumption caused by distributed decision-making, we propose Reinforcement Learning (RL) algorithm based on sequential alternating multi-agent system mechanism, which effectively reduces the dimensionality of action space without losing the informational content of action, achieving network lightweighting. We discuss the rationality, generalizability, and inherent advantages of proposed mechanism. Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability, generalizability, and adaptability to scenarios with invalid actions, all while achieving network lightweighting.

关键词： Vehicular edge computing Resource allocation Reinforcement learning Lightweight

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Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

Interpretation-Enabled Software Reuse Detection Based on a M...

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International Conference on Software Engineering (ICSE)

作者： Xi Xu Qinghua Zheng Zheng Yan Ming Fan Ang Jia Ting Liu Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi’an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland School of Cyber Science and Engineering Xi’an Jiaotong University China

ISBN: (纸本)9781665402965;9781665448314

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.

关键词： Law Semantics Libraries security Software reusability Software engineering Resilience

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