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Community-Preserving Social Graph Release with Node Differential Privacy

Journal of computer Science & Technology 2023年第6期38卷 1369-1386页

作者：张森倪巍伟付楠 School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 211189China

The goal of privacy-preserving social graph release is to protect individual privacy while preserving data *** structure,which is an important global pattern of nodes,is a crucial data utility as it is fundamental to many graph analysis ***,most existing methods with differential privacy(DP)commonly fall into edge-DP to sacri-fice security in exchange for ***,they reconstruct graphs from the local feature-extraction of nodes,resulting in poor community *** by this,we develop PrivCom,a strict node-DP graph release algorithm to maximize the utility on the community structure while maintaining a higher level of *** this algorithm,to reduce the huge sensitivity,we devise a Katz index based private graph feature extraction method,which can capture global graph structure features while greatly reducing the global sensitivity via a sensitivity regulation ***,under the condition that the sensitivity is fixed,the feature captured by the Katz index,which is presented in matrix form,requires privacy budget *** a result,plenty of noise is injected,mitigating global structural *** bridge this gap,we de-sign a private eigenvector estimation method,which yields noisy eigenvectors from extracted low-dimensional ***,a dynamic privacy budget allocation method with provable utility guarantees is developed to preserve the inherent relationship between eigenvalues and eigenvectors,so that the utility of the generated noise Katz matrix is well ***,we reconstruct the synthetic graph via calculating its Laplacian with the noisy Katz *** results confirm our theoretical findings and the efficacy of PrivCom.

关键词： differential privacy social graph community structure private eigenvector

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Efficient Registered Attribute Based Access Control with Same Sub-policies in Mobile Cloud Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Weng, Wuwei Li, Jiguo Zhang, Yichen Lu, Yang Shen, Jian Han, Jinguang Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing210023 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China Southeast University School of Cyber Science and Engineering Nanjing210096 China

Ciphertext-policy attribute-based encryption (CP-ABE) has long been considered as a promising access control technology for cloud storage. However, CP-ABE depends on a central trusted authority to generate and distribute decryption keys, resulting in the key escrow issue. Most existing solutions only mitigate this problem but fail to resolve it entirely. Registered attribute-based encryption (RABE), a new cryptographic primitive, fundamentally addresses the key escrow problem by modifying the trust model, but its high computational overhead limits its practical application. Inspired by this challenge, we present an efficient registered attribute-based access control scheme designed for data encrypted with access policies containing the same sub-policy. In our scheme, users generate their own keys, while a key manager, who does not hold keys, replaces the central authority in managing users. Additionally, for data encrypted with the same sub-policy, the user's initial decryption stores the relevant parameters, which can be used for subsequent decryptions to reduce computational overhead. The proposed scheme is proven to achieve semantic security. Performance analysis demonstrates that our scheme enhances decryption efficiency by roughly 41.4% compared to existing RABE scheme, with a minimal storage trade-off, making it more practical for cloud storage application. © 2002-2012 IEEE.

关键词： Ciphertext

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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Terahertz Nonvolatile Vectorial Holography With Random Repetitive Encryption and Nonreciprocal Janus Imaging Based on Magnetic Heterogeneous Integrated Metasurface

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Laser and Photonics Reviews 2025年

作者： Wang, Hao Fan, Fei Li, Pengxuan Xue, Qiang Tan, Zhiyu Zhao, Dan Zhao, Huijun Yang, Qinghui Wen, Qiye Chang, Shengjiang Institute of Modern Optics Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin300350 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Vectorial holography expands the information dimensionalities of the image, manifesting significant value in applications such as optical encryption, and polarization multiplexing. To address challenges like erasable storage, secret key leakage, and echo noise, a dynamic terahertz vectorial holography is demonstrated based on magnetic heterogeneous integrated metasurfaces with nonvolatility and nonreciprocity, whose polarization states vary continuously along the latitude of the whole Poincare Sphere with the experimental polarization ellipticity angles in the image covering −0.23π–0.21π rad, and can be stored or erased through nonvolatile magnetic rotation. This allows for the encryption with a random dynamic polarization secret key that can be regenerated indefinitely, greatly eliminating the risk of key leakage. The crosstalk between the orthogonal polarization states in the encryption is less than 0.0056. Meanwhile, leveraging the magneto-optical nonreciprocity, the metasurface functions as a Janus hologram with one-way isolation between forward and backward illumination, of which the image area proportion of over 5 dB isolation reaches 89%, effectively eliminating echo noise and further improving the anti-interference. This work addresses the gaps in terahertz dynamic vectorial holography, realizes the nonvolatile and nonreciprocal polarization information processing, and mitigates risks of secret key leakage and echo interference. © 2025 Wiley-VCH GmbH.

关键词： Magnetic leakage

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A Direction-Based Data Forwarding Algorithm for Opportunistic networks

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Journal of Electronic Science and Technology 2016年第2期14卷 152-159页

作者： Jie Yu Yue Ling Si-Ying Li Hui-Qi Fang Lin-Feng Liu School of Computer Science and Technology Nangjing University of Posts and Telecommunications Laboratory of Computer Network and Information Integration Southeast University

In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers （nodes） and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.

关键词： Accelerated delivery ratio,forwarding opportunistic networks storageoccupancies.

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Robust Dynamic Broadcasting for Multi-Hop Wireless networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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Building Privacy-Preserving Medical Text Models With a Pretrained Transformer

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IEEE Internet of Things Journal 2025年第9期12卷 11529-11538页

作者： Shafiq, Muhammad Ren, Lijing Srivastava, Gautam Zhang, Denghui Bourouis, Sami Gadekallu, Thippa Reddy Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou511370 China Institute of Artificial Intelligence Guangdong Mechanical & Electrical Polytechnic Guangzhou510515 China Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Division of Research and Development Lovely Professional University Phagwara144411 India Center of Research Impact and Outcome Chitkara University Rajpura140401 India

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis. © 2014 IEEE.

关键词： Differential privacy

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Measuring web page complexity by analyzing TCP flows and HTTP headers

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The Journal of China Universities of Posts and Telecommunications 2017年第6期24卷 1-13页

作者： Cheng Weiqing Hu Yangyang Yin Qiaofeng Chen Jiajia School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Computer Network and Information Integration Southeast University Jiangsu Education Information Center Jiangsu Provincial Department of Education

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels： transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol （TCP） flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.

关键词： hyper text transfer protocol concurrent TCP flows world wide web web page complexity

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User-Centric Data Management in Decentralized Internet of Behaviors System

arXiv

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arXiv 2025年

作者： Zhang, Shiqi Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China Chongqing Key Laboratory of Ubiquitous Sensing and Networking China Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University in New York City United States

The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This paper analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and using, while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74% and 56%, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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