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Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

OpenViewer: Openness-Aware Multi-View Learning

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

FRCNet: Frequency and Region Consistency for Semi-supervised Medical Image Segmentation

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arXiv 2024年

作者： He, Along Li, Tao Wu, Yanlin Zou, Ke Fu, Huazhu Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin China Haihe Lab of ITAI China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan Chengdu China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is important for lesion regions located at low frequencies and with significant scale changes. In this paper, we introduce two consistency regularization strategies for semi-supervised medical image segmentation, including frequency domain consistency (FDC) to assist the feature learning in frequency domain and multi-granularity region similarity consistency (MRSC) to perform multi-scale region-level local context information feature learning. With the help of the proposed FDC and MRSC, we can leverage the powerful feature representation capability of them in an effective and efficient way. We perform comprehensive experiments on two datasets, and the results show that our method achieves large performance gains and exceeds other state-of-the-art methods. © 2024, CC BY.

关键词： Deep learning

Information Dissemination Model Based on User Attitude and Public Opinion Environment

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arXiv 2024年

作者： Li, Xinyu Huang, Jinyang Zhang, Xiang Zhao, Peng Wang, Meng Zhuang, Guohang Yan, Huan Sun, Xiao School of Computer and Information Hefei University of Technology Hefei China CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei China School of Big Data and Computer Science Guizhou Normal University Guiyang China

Modeling the information dissemination process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information dissemination process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking the dynamic and evolving nature of user attitudes and the impact of the public opinion environment. In this paper, we propose a novel model, UAPE, which considers the influence of the aforementioned factors on the information dissemination process. Specifically, UAPE regards the user’s attitude towards the topic as dynamically changing, with the change jointly affected by multiple users simultaneously. Furthermore, the joint influence of multiple users can be considered as the impact of the public opinion environment. Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01%, surpassing the performance of existing research. Copyright © 2024, The Authors. All rights reserved.

关键词： Information dissemination

Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

DM-NAI: Dynamic Information Diffusion Model Incorporating Non-Adjacent Node Interaction

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DM-NAI: Dynamic Information Diffusion Model Incorporating No...

IEEE International Conference on Communications (ICC)

作者： Xinyu Li Jinyang Huang Xiang Zhang Peng Zhao Meng Wang Guohang Zhuang Huan Yan Xiao Sun School of Computer and Information Hefei University of Technology Hefei China CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei China School of Big Data and Computer Science Guizhou Normal University Guiyang China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Describing the dynamics of information diffusion within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information diffusion. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information diffusion process. Extensive experiments are conducted on six datasets to predict the information diffusion range and the diffusion trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information diffusion predictions.

关键词： Accuracy Diffusion processes Information processing Probability Market research Prediction algorithms Information diffusion

Scalable Client-side Encrypted Deduplication beyond Secret Sharing of the Master key

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Scalable Client-side Encrypted Deduplication beyond Secret S...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuchen Chen Guanxiong Ha Xuan Shan Chunfu Jia Qiaowen Jia The College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology and DISSec Tianjin China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Individuals and companies increasingly adopt encrypted deduplication systems for their enhanced security and efficiency benefits. Server-aided encrypted deduplication systems are the state-of-the-art scheme to resist brute-force attacks. However, it is overly reliant on a single centralized key server and vulnerable to a single point of failure. To this end, existing schemes have implemented distributed key servers based on secret sharing of the master key to resist a single point of failure. Nevertheless, this design has some inherent limitations in balancing security and scalability. Secret sharing of the master key effectively mitigates single points of failure, while negatively impacting system scalability. To address the above limitations, we propose a scalable client-side encrypted deduplication with distributed key servers based on secret sharing of the data key. To resist brute-force attacks, we also design a double-layer matching mechanism to achieve secure and effective duplicate check and key delivery. Additionally, drawing inspiration from random oracle models, we put forward a pseudo-random response strategy for key servers to safeguard key privacy effectively. Rigorous theoretical analysis and extensive experiments demonstrate that our scheme achieves both security and scalability, which is well-suited for deployment in large-scale systems and offers robust protection against a single point of failure.

关键词： Privacy data privacy Scalability Distributed databases Resists Brute force attacks Large-scale systems Cryptography Servers Protection

Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs

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arXiv 2024年

作者： Bai, Qijie Nie, Changli Zhang, Haiwei Dou, Zhicheng Yuan, Xiaojie The College of Computer Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

Heterogeneous graphs have attracted a lot of research interests recently due to the success for representing complex real-world systems. However, existing methods have two pain points in embedding them into low-dimensional spaces: the mixing of structural and semantic information, and the distributional mismatch between data and embedding spaces. These two challenges require representation methods to consider the global and partial data distributions while unmixing the information. Therefore, in this paper, we propose Dis-H2GCN, a Disentangled Hyperbolic Heterogeneous Graph Convolutional network. On the one hand, we leverage the mutual information minimization and discrimination maximization constraints to disentangle the semantic features from comprehensively learned representations by independent message propagation for each edge type, away from the pure structural features. On the other hand, the entire model is constructed upon the hyperbolic geometry to narrow the gap between data distributions and representing spaces. We evaluate our proposed Dis-H2GCN on five real-world heterogeneous graph datasets across two downstream tasks: node classification and link prediction. The results demonstrate its superiority over state-of-the-art methods, showcasing the effectiveness of our method in disentangling and representing heterogeneous graph data in hyperbolic spaces. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off

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arXiv 2023年

作者： Liu, Yu-An Zhang, Ruqing Zhang, Mingkun Chen, Wei de Rijke, Maarten Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hinders the widespread deployment of NRMs. By incorporating adversarial examples into training data, adversarial training has become the de facto defense approach to adversarial attacks against NRMs. However, this defense mechanism is subject to a trade-off between effectiveness and adversarial robustness. In this study, we establish theoretical guarantees regarding the effectiveness-robustness tradeoff in NRMs. We decompose the robust ranking error into two components, i.e., a natural ranking error for effectiveness evaluation and a boundary ranking error for assessing adversarial robustness. Then, we define the perturbation invariance of a ranking model and prove it to be a differentiable upper bound on the boundary ranking error for attainable computation. Informed by our theoretical analysis, we design a novel perturbation-invariant adversarial training (PIAT) method for ranking models to achieve a better effectiveness-robustness trade-off. We design a regularized surrogate loss, in which one term encourages the effectiveness to be maximized while the regularization term encourages the output to be smooth, so as to improve adversarial robustness. Experimental results on several ranking models demonstrate the superiority of PITA compared to existing adversarial defenses. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors