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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Zhang, Yadong Wang, Peng Wang, Qubeijian Zhang, Haibin Xu, Lexi Sun, Wen Wang, Bin School of Cybersecurity Northwestern Polytechnical University Xi’an China School of Cyber Engineering Xidian University Xi’an China Research Institute China United Network Communications Corporation Beijing China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity Hangzhou China

Wireless Computing Power Networks (WCPN), guided by green principles, aim to provide efficient, flexible, and environmentally friendly computing services for Internet of Things (IoT) applications by seamlessly coordinating computational and networking resources across diverse nodes. The integration of Digital Twin (DT) technology is crucial for achieving these objectives. However, different DT association strategies play a crucial role in enhancing the capabilities of WCPN. In this paper, recognizing the long-term and dynamic nature of DT deployment in real-world scenarios, we utilize evolutionary game theory to model the association and transfer of DTs, aiming for continuous adaptive adjustments and optimizations in their deployment. Specifically, we propose an evolutionary game-based algorithm for DT association as a complement to the independent decision-making process in DT deployment. Moreover, in light of the inherent limitations of the evolutionary game selection mechanism and the lack of self-learning ability, we introduce a deep Q-network (DQN) based evolutionary game approach that ensures adaptive DT association and transfer by considering factors such as DT synchronization delay, model consistency, and migration costs. Numerical results demonstrate that our proposed algorithms outperform the benchmarks in terms of average user utility and convergence speed. IEEE

关键词： Games Computational modeling Wireless communication Internet of Things Adaptation models Servers Real-time systems

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Towards robust data augmentation for sequence recommendation 16

Towards robust data augmentation for sequence recommendation

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Chen, Yan Zhao, Sunhang Wang, Xinyuan Jin, Zhengxuan Lin, Zekai Wang, Bin The Third Research Institute of the Ministry of Public Security Shanghai China College of Letters and Science United States Zhejiang Gongshang University Computer Science and Technology China Binjiang Institute of Zhejiang University China Zhengzhou University School of Computer and Artificial Intelligence China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity China

ISBN: (纸本)9798331506896

This paper presents RAS-Rec, a novel and robust approach to sequence recommendation that leverages the power of data augmentation techniques. RAS-Rec proposes data augmentation as a means to enhance the representations of users and items in sequence recommendation. By generating synthetic training samples through various transformations applied to the original data, RAS-Rec increases the diversity and quantity of the training set. The augmented sequence data addresses the limitations of existing methods and improves the robustness and generalization capabilities of sequence recommendation models. Additionally, a contrastive learning scheme is designed to train and further enhance the representations in RAS-Rec. Extensive experiments validate the effectiveness of the proposed approach, demonstrating its superiority over existing methods in terms of recommendation performance. The results highlight the value of data augmentation and contrastive learning in sequence recommendation tasks. The findings of this study contribute to the advancement of robust sequence recommendation techniques. © 2024 IEEE.

关键词： Contrastive Learning

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Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

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Computer Modeling in Engineering & Sciences 2024年第11期141卷 1305-1325页

作者： Yongsheng Zhu Chong Liu Chunlei Chen Xiaoting Lyu Zheng Chen Bin Wang Fuqiang Hu Hanxi Li Jiao Dai Baigen Cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Institute of Computing Technologies China Academy of Railway Sciences Corporation LimitedBeijing100081China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing100044China Institute of Infrastructure Inspection China Academy of Railway Sciences Corporation LimitedBeijing100081China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and CybersecurityHangzhou310053China

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system *** learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private ***,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s *** this study,we introduce PMM(Perturbation coefficient multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated *** experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s ***,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

关键词： Privacy-preserving intelligent railway transportation system federated learning poisoning attacks defenses

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Model-Driven Channel Extrapolation for Massive Fluid Antenna

Model-Driven Channel Extrapolation for Massive Fluid Antenna

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IEEE International Conference on Communications Workshops, ICC

作者： Guo Li Haibin Zhang Chengcai Wang Bin Wang School of Cyber Engineering Xi-dian University Xi'an China School of Mechanical Engineering Beihang University Beijing China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Hangzhou China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Fluid antenna (FA), with its unique adaptability and precise signal control capabilities, have emerged as potential components for the future of wireless communication. However, the massive number of ports in FA poses challenges in channel state information (CSI) estimation. Existing methods, predominantly relying on deep learning (DL)-based channel extrapolation, face limitations in acquiring CSI for all antenna ports and suffer from generalization problem caused by sequence-dependent model. In this paper, we propose a model-driven channel extrapolation algorithm based on the theoretical analysis of Jake's channel model. Diverging from existing DL-based approaches, our method builds on theoretical and empirical analyses of channel correlations in Jake's model, allowing for robust training with minimal observed ports and ensuring robust channel extrapolation across all ports on FA. Numerical experiments validate the effectiveness and robustness of our proposed algorithm, achieving a low normalized mean square error (NMSE) down to -30 dB in channel extrapolation with just 30 observed ports during training, even as the total number of ports reaches ten thousand.

关键词： Training Deep learning Wireless communication Extrapolation Analytical models Fluids Conferences

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Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Hey! You Photographed My Screen without Approved: A Practica...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Liu, Kui Wang, Bin Shi, Ziwei Chen, Jiadong Zhang, Zhiwei School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Network and Information Security Laboratory Hangzhou Hikvision Digital Technology Co. LTD. Hangzhou China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security. © 2022 IEEE.

关键词： Digital storage

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PAGE: Equilibrate Personalization and Generalization in Federated Learning

arXiv

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arXiv 2023年

作者： Chen, Qian Wang, Zilong Hu, Jiaqi Yan, Haonan Zhou, Jianying Lin, Xiaodong State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Zhejiang China iTrust Singapore University of Technology and Design Singapore Singapore School of Computer Science University of Guelph Guelph Canada

Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients’ current demands and the server’s future demand, local model personalization and global model generalization are separately investigated, as the ill-effects of data heterogeneity enforce the community to focus on one over the other. However, these two seemingly competing goals are of equal importance rather than black and white issues, and should be achieved simultaneously. In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. To explore the equilibrium, PAGE further formulates the game as Markov decision processes, and leverages the reinforcement learning algorithm, which simplifies the solving complexity. Extensive experiments on four widespread datasets show that PAGE outperforms state-of-the-art FL baselines in terms of global and local prediction accuracy simultaneously, and the accuracy can be improved by up to 35.20% and 39.91%, respectively. In addition, biased variants of PAGE imply promising adaptiveness to demand shifts in practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： multilayer neural networks

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Bfs2adv: Black-Box Adversarial Attack Towards Hard-to-Attack Short Texts

SSRN

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SSRN 2023年

作者： Han, Xu Li, Qiang Cao, Hongbo Wang, Bin Bao, Xuhua Han, Yufei Wang, Wei Beijing Key Laboratory of Security and Privacy of Intelligent Transportation Beijing Jiaotong University Beijing100044 China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Zhejiang University Hangzhou310027 China Sangfor Technologies Inc Shenzhen518055 China INRIA Bretagne Rennes35042 France

The advent of Machine Learning as a Service (MLaaS) and deep learning applications has increased the susceptiblility of models to adversarial textual attacks, particularly in black-box settings. Prior work on black-box adversarial textual attacks generally follows a stable strategy that involves leveraging char-level, world-level, and sentence-level perturbations, as well as using queries to the target model to find adversarial examples in the search ***, existing approaches prioritize query efficiency by reducing the search space, thereby overlooking hard-to-attack textual instances. To address this issue, we propose \textit{BFS2Adv}, a brute force algorithm that generates adversarial examples for both easy-to-attack and hard-to-attack textual inputs. Specifically, given an original text, BFS2Adv uses word-level perturbations and word synonym substitution to create a complete search space, where each node is a candidate adversarial example. BFS2Adv then explores the search space using breadth-first search and queries to the target model to find the qualified adversarial examples. A prototype of BFS2Adv was implemented and evaluated against popular models ALBERT and BERT using the SNLI and MR datasets. Our results demonstrate that BFS2Adv outperforms state-of-the-art algorithms and effectively improves the success rate of short-text adversarial attacks. Furthermore, we provide detailed insights into the robustness of BFS2Adv by analyzing those hard-to-attack examples. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Towards robust data augmentation for sequence recommendation

Towards robust data augmentation for sequence recommendation

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Yan Chen Sunhang Zhao Xinyuan Wang Zhengxuan Jin Zekai Lin Bin Wang The Third Research Institute of the Ministry of Public Security Shanghai China College of Letters and Science University of California Santa Barbara (UCSB) Computer Science and Technology Zhejiang Gongshang University China Binjiang Institute of Zhejiang University China School of Computer and Artificial Intelligence Zhengzhou University China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

关键词：

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Two-in-One Solution: Simultaneously Enhancing Security and Privacy for Data-Driven Models in Mobile Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Liu, Pengrui Yuan, Xiaohan Wang, Wei Xu, Xiangrui Li, Tao Wang, Junyong Wang, Bin Pedrycz, Witold Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation 3 Shangyuancun Beijing100044 China Haihe Lab of ITAI Tianjin300450 China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Data-driven models are widely employed in Mobile Edge Computing to satisfy the demands of Emerging Consumer Applications. However, previous work demonstrates that data-driven models are susceptible to security threats like Backdoor and Evasion Attacks or privacy threats like Membership Inference Attacks. Numerous existing methods for mitigating these threats have been proposed. However, these methods focus solely on enhancing model security or only on improving model privacy. Ideally, data-driven models should enhance model security and privacy simultaneously. In this paper, we propose methods that combine individual security-enhancing and privacy-enhancing methods to mitigate the security and privacy threats of data-driven models simultaneously. We evaluate the effectiveness of individual security-enhancing methods, individual privacy-enhancing methods, and our methods in simultaneously enhancing model security and privacy. Our comprehensive experimental analysis reveals two-fold insights. First, individual security-enhancing methods can either enhance or diminish model privacy, while individual privacy-enhancing methods face challenges in enhancing model security. Second, our methods improve the effectiveness of simultaneously enhancing model security and privacy compared to the individual methods. © 1975-2011 IEEE.

关键词： Differential privacy

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