Privacy-Aware Access Control in IoT-Enabled Healthcare: A Federated Deep Learning Approach

计算机科学 访问控制 物联网 医疗保健 控制(管理) 计算机安全 数据存取 服务器 互联网隐私 信息隐私 计算机网络 数据库 人工智能 经济增长 经济
作者
Hui Lin,Kuljeet Kaur,Xiaoding Wang,Georges Kaddoum,Jia Hu,Mohammad Mehedi Hassan
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:10 (4): 2893-2902 被引量:33
标识
DOI:10.1109/jiot.2021.3112686
摘要

The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet-of-Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this article, an attribute-based secure access control mechanism, coined (SACM), is proposed for IoT-Health utilizing the federated deep learning (FDL). Specifically, we manage to discover the relationship between users' social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible–infected–recovered model-based loss function, users' influences are obtained and then are transformed to their trusts. For each occupation, users' trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the FDL is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experimental results show that the proposed SACM achieves accurate access control in IoT-enabled healthcare with high data integrity and low privacy leakage.

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