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

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.

Automated summary

The traditional healthcare is facing challenges in processing and storing massive medical data. The emergence of Internet-of-Things (IoT) technologies provides an opportunity for the healthcare industry to transition into IoT-enabled healthcare with enhanced data storage and processing capabilities. However, privacy leakage and data tampering by unauthorized users pose significant risks in IoT-enabled healthcare. This article proposes an attribute-based secure access control mechanism, utilizing federated deep learning (FDL), to address these concerns and ensure accurate access control and enhanced privacy preservation.