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SSRN 2023年

作者： Jiang, Rong Liu, Rui Zhang, Tao Ding, Weiping Tian, Shenghu Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China School of Information Science and Technology Nantong University Nantong China

Electronic Medical Record (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and using, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet the dynamic and complex healthcare environment. Therefore, this article proposes an intuitionistic fuzzy trust-based access control model to solve the data privacy leakage problem in order to promote medical information sharing. The model proposes a new trust evaluation method that uses Latent Dirichlet Allocation (LDA) topic model and intuitionistic fuzzy theory to more accurately quantify doctors' trust;In trust aggregation, time windows and time decay are used to enhance the timeliness of trust aggregation;The model also adopts an intuitionistic fuzzy clustering algorithm based on relative entropy and a reward and punishment mechanism to achieve adaptive dynamic access control of doctors' access behavior. Experiments prove that the access control model based on intuitionistic fuzzy trust proposed in this article has a certain control effect on the privacy leakage problem of electronic medical record system. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Security Analysis for a VANET Privacy Protection Scheme

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Journal of Cyber Security 2022年第1期4卷 57-64页

作者： Yuzhen Liu Xiaoliang Wang Zhoulei Cao Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201China School of Engineering and IT University of New South WalesNSWAustralia

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing *** to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and *** of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of *** propose a scheme based on fair blind signature and secret sharing *** this paper,we prove that the scheme is feasible through security analysis.

关键词： Vehicular ad hoc network anonymous authentication fair blind signature secret sharing algorithm security analysis BAN logic ideology

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Neuron Circuit Design and Signal Integrity Analysis of a Large-scale Memristor-based Crossbar Array

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IEEE Transactions on Components, Packaging and Manufacturing technology 2025年

作者： Li, Yan Fang, Lidan Li, Da Zhang, Ling Zhou, Haomiao Wang, Jiawei Liu, En-Xiao Li, Er-Ping China Jiliang University Key Laboratory of Electro-magnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering Hangzhou310018 China China Jiliang University Modern Science and Technology College Yiwu322000 China Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart System Hangzhou310027 China Urbana–Champaign Institute Zhejiang University Zhejiang University–the University of Illinois Haining314400 China A*STAR Institute of High Performance Computing 1 Fusionpolis Way Singapore Singapore

This paper addresses the limitations of standard chips in processing large-scale datasets by leveraging neuromorphic architectures, particularly spiking neural networks (SNNs), to simulate the pulsed signals of biological brains for enhanced power efficiency and performance. Initially, we developed a CMOS circuit based on the Integrate-and-Fire (IF) neuron model, which generates spike signals with the spatiotemporal dynamics typical of neurons. This approach provides a novel perspective for neuromorphic chip design. Next, we introduce the Partial Element Equivalent Circuit (PEEC) method to establish a unit circuit model for the memristor crossbar array, accounting for all resistance-inductance-capacitance (RLC) parasitic couplings. This model enables precise signal integrity (SI) analysis, offering deeper insights into the signal transmission mechanisms within memristor crossbar arrays and serving as a foundation for optimizing neuromorphic chip performance. Building upon this model, we designed a 24×24 crossbar array hardware prototype, experimentally validating the reliability and feasibility of the circuit. Finally, we perform SI analysis to demonstrate that the performance of the memristor crossbar array can be enhanced by optimizing the rise time, adjusting the memristor’s resistance state, and adding inductance at the output. These findings provide both a theoretical foundation and a technical pathway for improving the performance of neuromorphic chips in practical applications. © 2025 IEEE. All rights reserved.

关键词： Memristors

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A Novel Privacy-Preserving Shared Charging Pile for Online Electric Vehicle Matching system

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SSRN 2022年

作者： Wang, Xiaoliang Huang, Zhiyu Xiong, Neal Naixue Zeng, Peng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Northeastern State University United States

With the increase of electric vehicles, the demand of electric vehicles for charging piles is increasing, but the number of charging piles is far less than that of electric vehicles, especially in rural areas and underdeveloped areas. We can effectively solve the problem of electric vehicle power supply demand by sharing private charging piles by using the characteristics of V2V technology. Ordinary online matching charging piles need to upload data to the server. In the process of matching, the data may be attacked, which may expose users' privacy information, and privacy protection is also a concern of most users now, users often worry that the leakage of location information will lead to their privacy invasion. To solve this problem, this paper proposes an anonymous matching scheme for shared charging piles. The system uses homomorphic encryption technology to encrypt the privacy information of users, and completes the matching between electric vehicles and charging piles on the premise of protecting users' privacy. In the proxy server, the data is encrypted, and in the cloud server, the data is the result of interference, so the privacy of users can be effectively protected. We also set up a new set of matching rules. © 2022, The Authors. All rights reserved.

关键词： Piles

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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Joint Data Retransmission and Client Selection Optimization for Error-Tolerant Federated Learning in UAV Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Dong, Chao Wu, Feiyu Qu, Yuben Akerkar, Rajendra Wu, Tao Zhou, Fuhui Wu, Qihui Guo, Song Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing210023 China Western Norway Research Institute Sogndal6856 Norway Hong Kong Polytechnic University Hong Kong Hong Kong University of Science and Technology 999077 Hong Kong The National University of Defense Technology Department of Computing Anhui Hefei230009 China

The flexibility and mobility of Unmanned Aerial Vehicle (UAV) swarms enable them to integrate with federated learning (FL), an emerging distributed machine learning framework. UAV-FL creates an edge intelligence system for UAV swarms. However, unreliable UAV networks lead to a large amount of retransmissions for the dropped data, making it difficult for FL in UAV swarms to achieve the expected accuracy within a short period. Fortunately, benefiting from the dilution effect of convolutional computations, FL can tolerate a limited amount of model parameter errors. Thus motivated, this paper proposes a novel FL framework called FedET, to jointly optimize data retransmission and client selection to achieve error-tolerant FL in UAV networks. Specifically, FedET utilizes error tolerance of FL to reduce the number of retransmissions, which navigates the trade-off between training time and model accuracy. Meanwhile, the impact of retransmission on client selection is also analyzed. We formulate the training utility maximization problem for FL via jointly optimizing data retransmission and client selection. To solve this problem, we propose an alternating optimization-based algorithm to reach the local optimal solution. We implement and evaluate FedET on widely used real-world UAV embedded devices (i.e., NVIDIA Jetson devices). Compared to existing FL algorithms, when faced with unreliable UAV networks, FedET on average reduces the total training time by ∼45.8%, and transmission latency by ∼67.9%, respectively. © 2015 IEEE.

关键词： Federated learning

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Prototype Guided Personalized Federated Intrusion Detection system

Prototype Guided Personalized Federated Intrusion Detection ...

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Smart World Congress (SWC), IEEE

作者： Long Cheng Huiru Yan Hanlin Zhou Ying Wang Haichuan Tang Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China AI Lab of CRRC Academy Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines.

关键词： Training Privacy Accuracy Federated learning Image edge detection Prototypes Intrusion detection Data models Ensemble learning Internet of Things

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