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CRP2-VCS: Contrast-Oriented Region-based Progressive Probabilistic Visual Cryptography Schemes

IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5501-5517页

作者： Wu, Xiaotian Song, Bofan Fang, Jia Yan, WeiQi Peng, Qing-Yu Jinan University Guangdong Key Laboratory of Data Security and Privacy Preserving College of Cyber Security College of Information Science and Technology Guangzhou China Auckland University of Technology Department of Computer and Information Systems Auckland New Zealand Jinan University Sino-French Joint Laboratory for Astrometry Dynamics and Space Science Guangzhou China

Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique. © 2025 IEEE.

关键词： Visualization Cryptography Probabilistic logic Interference Encoding Circuits and systems Polynomials Optimization Minimax techniques Computational modeling

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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain Data security Storage Management Utilization

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Residual Tensor Train: A Quantum-Inspired Approach for Learning Multiple Multilinear Correlations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1101-1113页

作者： Chen, Yiwei Pan, Yu Dong, Daoyi Zhejiang University Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China University of New South Wales School of Engineering and Information Technology CanberraACT2600 Australia

States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modeled. In machine learning, complex multiple multilinear correlations may also exist within input features. In this article, we present a quantum-inspired multilinear model, named residual tensor train (ResTT), to capture the multiple multilinear correlations of features, from low to high orders, within a single model. ResTT is able to build a robust decision boundary in a high-dimensional space for solving fitting and classification tasks. In particular, we prove that the fully connected layer and the Volterra series can be taken as special cases of ResTT. Furthermore, we derive the rule for weight initialization that stabilizes the training of ResTT based on a mean-field analysis. We prove that such a rule is much more relaxed than that of tensor train (TT), which means that ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models. Numerical experiments demonstrate that ResTT outperforms the state-of-the-art tensor network and benchmark deep learning models on MNIST and Fashion-MNIST datasets. Moreover, ResTT achieves better performance than other statistical methods on two practical examples with limited data, which are known to have complex feature interactions. © 2020 IEEE.

关键词： Neural networks

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Stone Soup: No Longer Just an Appetiser 26

Stone Soup: No Longer Just an Appetiser

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26th International Conference on information Fusion, FUSION 2023

作者： Hiscocks, Steven Barr, Jordi Perree, Nicola Wright, James Pritchett, Henry Rosoman, Oliver Harris, Michael Gorman, Roisín Pike, Sam Carniglia, Peter Vladimirov, Lyudmil Oakes, Benedict Defence Science and Technology Laboratory Cyber and Information Systems Salisbury United Kingdom Defence Research and Development Canada Radar Sensing and Exploitation Ottawa Canada University of Liverpool Electrical Engineering & Electronics Cdt in Distributed Algorithms Liverpool United Kingdom

ISBN: (纸本)9798890344854

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community *** Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable *** announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community. © 2023 International Society of information Fusion.

关键词： State estimation

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Delegatable Multi-Authority Attribute-based Anonymous Credentials

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IEEE Transactions on Cloud Computing 2025年第02期13卷 655-666页

作者： Sun, Meng Lai, Junzuo Mo, Xiaohan Wu, Chi Li, Peng Chu, Cheng-Kang Deng, Robert H. Jinan University College of Cyber Security Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China Tsinghua University Institute of Trustworthy Networks and Systems School of Software Beijing100084 China Digital Identity and Trustworthiness Laboratory Huawei Singapore 138588 Singapore Singapore Management University School of Computing and Information Systems 188065 Singapore

In cloud computing, users need to authenticate to access various resources. Attribute-based anonymous credentials (ABCs) provide a tool for privacy-preserving authentication, allowing users to prove possession of a set of attributes to cloud service providers anonymously. Most existing works on ABC deal with credentials on attributes issued by a single authority (issuer). In reality, it is more practical for users to obtain credentials on attributes from multiple authorities. There are a few works on multi-authority ABC, which do not support delegation needed in real deployments. In this paper, we present the first delegatable multi-authority attribute-based anonymous credential system, which simultaneously achieves revocation and traceability. We also give the security analysis of our construction. Finally, we implement our system, and the experimental results show its efficiency. © 2013 IEEE.

关键词： Anonymity

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Implementation of Non-Gaussian Motion Models Within Stone Soup

Implementation of Non-Gaussian Motion Models Within Stone So...

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International Conference on information Fusion

作者： Zhen Yuen Chong Henry Pritchett Qing Li Runze Gan Yaman Kındap Simon Godsill Department of Engineering University of Cambridge Cambridge UK Cyber and Information Systems Defence Science and Technology Laboratory Salisbury UK

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In recent years, state-space models for highly manoeuvrable objects have been proposed based on non-Gaussian, continuous time, jump-based Lévy processes, the so-called Lévy state-space model [1]–[4]. In these models, the standard Brownian motion driving process for continuous time processes is replaced with a heavy-tailed non-Gaussian alternative. This retains all the flexibility of its Gaussian counterpart in terms of possible dynamical model structures and operations with irregular time stamps or heterogeneous data sources. These models aim to operate in areas such as surveillance of irregularly moving drones or people, and tracking wildlife or biological data. Implementation is relatively straightforward since the Kalman filters of the Brownian motion case can be replaced in the nonGaussian case by mixtures of Kalman filters within a marginalised particle filtering framework [5]. While the Stone Soup tracking software environment includes both Kalman filtering and generic particle filtering, it does not currently allow the combination of these tasks within a marginalised particle filtering framework. We discuss the significant challenges involved in incorporating these models and algorithms into Stone Soup, and present initial simulation results for the new software.

关键词： Tracking Biological system modeling Surveillance Wildlife Filtering algorithms information filters Mathematical models Data models State-space methods Kalman filters

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Protecting Multi-Domain Command and Control information System Network Traffic with Segment Routing 1

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10th China Conference on Command and Control, C2 2022

作者： Yuan, Zongyang Chen, Tao Ren, Bangbang Luo, Lailong The Science and Technology on Information Systems Engineering Laboratory National University of Defence Technology Changsha China

ISBN: (数字)9789811960529

ISBN: (纸本)9789811960512

In the Multi-Domain Command and Control information System (MDCCIS), data security and privacy protection play key roles in improving network security. However, some network devices may be illegally hijacked, which may result in data leakage. To solve this problem, Avoidance Routing (AR) which bypasses the untrusted network devices has been proposed to protect the traffic. However, existing methods have to deploy agent nodes in the network, which confines the selection of routing paths and degrades the flexibility of the network Recently, a new source routing technology, Segment Routing (SR), has been proposed to steer flows along any path as network operators expected. In this paper, we leverage a new mechanism of SR to find proper avoidance routing paths for traffic protection. We formulate this problem as an integer programming model and propose two algorithms to get a feasible solution with different security Service Level Agreement (SLA) constraints. Experimental results show that our C-SAR algorithm protects the traffic with a 95 % success rate in low-risk networks and the P-SAR algorithm can realize a 50 % success rate even in uncertain networks. © 2022, Chinese Institute of Command and Control.

关键词： Integer programming

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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