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SSRN 2023年

作者： Zhu, Liehuang Xie, Yumeng Zhou, Yuao Fan, Qing Zhang, Chuan Liu, Ximeng School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou350108 China

The Healthcare Internet of Things (HIoT) system enables remote monitor patient health state and feedback on medical treatments timely. The success of HIoT relies on how to share health data securely and efficiently. Existing solutions either fail to ensure the security or incur significant overheads. In this paper, we put forward an efficient and secure health data sharing scheme for HIoT systems, named ESDS, to solve this dilemma. In ESDS, we first signcrypt the health data collected by IoT devices to protect patient privacy and ensure data authenticity. Next, we aggregate N signatures for N pieces of different health data into a short signature, which significantly reduces storage space and transmission size, i.e., from O(N) to O(1). Then, we apply local verifiability to verify one piece of health data under the aggregated signature. With this property, a doctor only needs one auxiliary message generated by the patient to check the target message without obtaining whole health data, which improves system verification efficiency. Extensive security analysis and performance evaluation show that ESDS is secure and efficient. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Using information entropy to analyze secure multi-party computation protocol

Using information entropy to analyze secure multi-party comp...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Yun Luo Yuling Chen Tao Li Yilei Wang Yixian Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science Qufu Normal University Rizhao China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665421751

Secure multi-party computation(SMPC) is an important research field in cryptography, secure multi-party computation has a wide range of applications in practice. Accordingly, information security issues have arisen. Aiming at security issues in Secure multi-party computation, we consider that semi-honest participants have malicious operations such as collusion in the process of information interaction, gaining an information advantage over honest parties through collusion which leads to deviations in the security of the protocol. To solve this problem, we combine information entropy to propose an n-round information exchange protocol, in which each participant broadcasts a relevant information value in each round without revealing additional information. Through the change of the uncertainty of the correct result value in each round of interactive information, each participant cannot determine the correct result value before the end of the protocol. security analysis shows that our protocol guarantees the security of the output obtained by the participants after the completion of the protocol.

关键词： Analytical models Protocols Uncertainty Computational modeling Information security Big data Multi-party computation

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A Comprehensive System for 3d Display: From Image Capture to Autostereoscopic Playback-Recommended by Chinamm

SSRN

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SSRN 2023年

作者： Wen, Chen Di, Zhang Ma, JingJing Wu, GuoWen School of Data Science and Media Intelligence Communication University of China Beijing100024 China National Key Laboratory of Media Convergence and Communication School of Data Science and Media Intelligence Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China

The production and playback processes involved in rendering 3D content exhibit a notable degree of intricacy and inefficiency. This article presents a comprehensive array of remedies aimed at addressing challenges pertaining to the acquisition, processing, and display of such content. The proposed framework is strategically compartmentalized into three key modules: the depth map generation and optimization module, the multi-viewpoint generation and optimization module, and the 3D content encoding and display module. Within the domain of depth map generation and optimization, we introduce a self-supervised convolutional network that builds upon the PSMNet architecture. This network serves the purpose of extracting accurate depth data. In the realm of multi-viewpoint generation and optimization, we put forth a novel partial convolutional neural network founded upon the Edge-PUnet framework. This network is specifically designed to enhance virtual viewpoint images. The efficacy of the depth data generation algorithm is substantiated by achieving an Out-Noc value of 2.27% on the KITTI-2012 dataset. Furthermore, the virtual viewpoint optimization algorithm yields notable outcomes, as evidenced by attained PSNR and SSIM values of 32.688 and 0.932, respectively, in the context of a street dance sequence. In summation, the outcomes of our study suggest that the implemented system holds promise for elevating the efficiency of 3D content production while concurrently curbing associated costs. © 2023, The Authors. All rights reserved.

关键词： Stereo image processing

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Model-Based Throughput Optimization for Blockchain Sharding System

Model-Based Throughput Optimization for Blockchain Sharding ...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yifan Li Jianxiong Wan Chuyi Liu Leixiao Li School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People’s Republic of China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The throughput issue has become an urgent problem for large-scale application of blockchain technology. Sharding technology can improve system throughput and scalability. Current research on the optimal blockchain sharding problem uses Model-Free Reinforcement Learning (MFRL) to maximize system throughput. However, these methods learn directly from interactions with the system, resulting in extremely low sampling efficiency. To bridge this gap, this paper presents a Model-based Policy Optimization Blockchain Sharding algorithm (MBPOBS), which utilizes the Gaussian Process Regression (GPR) to precisely predict the future states, based on which the best sharding strategy is learned through imitation learning from the Cross-Entropy Method (CEM) demonstrator. Simulation results show that compared to traditional MRFL algorithms, MBPOBS accelerates the learning by up to 1.8x.

关键词： Sharding Simulation Scalability Imitation learning Reinforcement learning Predictive models Throughput Prediction algorithms Blockchains Optimization

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Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

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Physical Review E 2020年第4期102卷 042314-042314页

作者： Yang Ye Qingpeng Zhang Zhongyuan Ruan Zhidong Cao Qi Xuan Daniel Dajun Zeng School of Data Science City University of Hong Kong Hong Kong SAR China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen Artificial Intelligence and Data Science Institute Shenzhen Guangdong China

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

关键词： Epidemic Spreading Multilayer & multiplex networks Social networks

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Collaborative Resource Allocation for Blockchain-Enabled Internet of Things with Multi-Agent Deep Reinforcement Learning

Collaborative Resource Allocation for Blockchain-Enabled Int...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xinyu Lu Jianxiong Wan Leixiao Li Chuyi Liu Xiaowei Si School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Ho-hhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Mobile Edge Computing (MEC) reduces service latency and enhances Quality of Service (QoS) by offloading tasks to the wireless network edge. However, the rapid growth of task offloading and the associated data transmission security challenges deserve further investigation. This study proposes a blockchain-MEC hybrid solution where mobile devices process tasks and engage in block mining to boost system utility. The objective is to maximize the accumulated reward in the blockchain-MEC system by optimizing off loading decisions, channel selection, transmission power, computing resources, and block intervals. A Markov Decision Process is formulated to model the optimization problem which is solved via a multi-agent deep reinforcement learning (MADRL) algorithm. The results of the simulation demonstrate that our approach is more effective than the baseline method.

关键词： Wireless networks Collaboration Quality of service Deep reinforcement learning Blockchains Resource management Internet of Things data mining security Optimization

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Quantum adversarial metric learning model based on triplet loss function

arXiv

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arXiv 2023年

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China College of Information Science and Engineering ZaoZhuang University ZaoZhuang Shandong277160 China School of Cyberspace Security Security Beijing University of Posts Telecommunications Beijing100876 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang 550025 China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher ∊-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz

arXiv

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arXiv 2023年

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China College of Information Science and Engineering ZaoZhuang University Shandong Zaozhuang277160 China School of Cyberspace Security Security Beijing University of Posts Telecommunications Beijing100876 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang550025 China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher Ε-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning. © 2023, CC BY.

关键词： Machine learning

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Gnn-Mgrpool: Enhanced Graph Neural Networks with Multi-Granularity Pooling for Graph Classification

SSRN

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SSRN 2023年

作者： Sun, Haichao Wang, Guoyin Liu, Qun Guo, Yike Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Engineering and Science Shanghai University Shanghai200444 China

Graph neural networks (GNNs) have gained significant attention and have been applied in various domain tasks. Currently, numerous pooling approaches have been proposed to aggregate node features and obtain node embeddings. However, current GNNs are almost black-box models that typically use a flat or single pooling step to aggregate nodes by only considering the similarity between nodes within the cluster. These approaches ignore the influence of relationships within and between clusters. To address this issue, we propose a novel multi-granular pooling method to aggregate nodes by simultaneously considering density and relationships among nodes and clusters. This method allows to obtain multi-granular node embedding clusters. The clusters in the current layer are built upon the previous layer, and these clusters change from fine to coarse as the number of clusters decreases, which is achieved by using multi-granular pooling (MgrPool for short). Additionally, the node representation of each layer is established through the ratio of node distance within clusters to that of between clusters. Finally, we conduct several experiments on node and graph classification tasks by this pooling approach. The results demonstrate that our GNN-MgrPool model outperforms the state-of-the-art similar algorithms and largely improves the interpretability of the learning process. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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A New Multi-Receiver Authentication Scheme for General Access Structure

A New Multi-Receiver Authentication Scheme for General Acces...

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IEEE International Symposium on Information Theory

作者： Jing Yang Shu-Tao Xia Xianfang Wang Can Xiang Fang-Wei Fu Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China CETC Cyberspace Security Technology Co. Ltd. Beijing China College of Mathematics and Informatics South China Agricultural University Guangzhou Guangdong China Tianjin Key Laboratory of Network and Data Security Technology Chern Institute of Mathematics and LPMC Nankai University Tianjin China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capabilities of all receivers are the same. However, in reality, different receivers may need to have different storage capabilities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where distinct participants save shares with distinct sizes, we propose a new unconditionally secure MRA scheme for multiple messages by the same technique. As far as we know, our MRA scheme is the first MRA scheme with different storage capacities for different receivers and the first one based on the CRT for polynomial rings supporting general access structures. In contrast to the existing general MRA scheme, although our MRA scheme has more communication complexity, it has less computation complexity.

关键词： Structural rings Authentication Receivers Cathode ray tubes Polynomials Cryptography Computational complexity

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