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智能手表中一种细粒度的用户划分隐私保护访问控制协议

A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch.

作者信息

Fang Liming, Li Minghui, Zhou Lu, Zhang Hanyi, Ge Chunpeng

机构信息

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, No. 29 Yudao Street, Nanjing 210016, China.

Key Laboratory of Computer Network Technology of Jiangsu Province, Nanjing 210096, China.

出版信息

Sensors (Basel). 2019 May 7;19(9):2109. doi: 10.3390/s19092109.

DOI:10.3390/s19092109
PMID:31067751
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6539881/
Abstract

A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.

摘要

智能手表是物联网中一种新兴的可穿戴设备。安全和隐私问题是阻碍智能手表广泛部署的主要障碍。现有的安全机制未能在隐私保护和数据访问控制之间实现平衡。在本文中,我们提出了一种用于智能手表的细粒度隐私保护访问控制架构(FPAS)。在FPAS中,我们利用基于身份的认证方案来保护设备免受恶意连接,并利用基于策略的访问控制来保护数据隐私。FPAS的核心策略有两个方面:(1)利用同态和重新加密方案确保密文信息能够被正确计算;(2)根据不同属性对数据请求者进行划分以避免未经授权的访问。我们基于上述原型提出了一个具体方案,并分析了FPAS的安全性。性能和评估表明,FPAS方案是高效、实用且可扩展的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/534f1327a408/sensors-19-02109-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/aa317fd5e2a9/sensors-19-02109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/c91527282929/sensors-19-02109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/1a6457ca2c40/sensors-19-02109-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/ba075b0c2991/sensors-19-02109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/49b0502acf99/sensors-19-02109-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/baeed2ba470d/sensors-19-02109-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/534f1327a408/sensors-19-02109-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/aa317fd5e2a9/sensors-19-02109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/c91527282929/sensors-19-02109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/1a6457ca2c40/sensors-19-02109-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/ba075b0c2991/sensors-19-02109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/49b0502acf99/sensors-19-02109-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/baeed2ba470d/sensors-19-02109-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6feb/6539881/534f1327a408/sensors-19-02109-g007.jpg

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SecureMed: Secure Medical Computation Using GPU-Accelerated Homomorphic Encryption Scheme.SecureMed:使用 GPU 加速同态加密方案进行安全医疗计算。
IEEE J Biomed Health Inform. 2018 Mar;22(2):597-606. doi: 10.1109/JBHI.2017.2657458. Epub 2017 Jan 23.