Privacy-Preserving Services for Internet of Medical Things: Architecture, Techniques, and Challenges

Abstract

The Internet of Medical Things (IoMT) has attracted the attention of many scholars because of its revolutionary impact on healthcare services. However, the sensitivity of medical data and the complexity of IoMT architectures pose critical challenges to privacy-preserving service design. This survey offers a comprehensive and novel investigation and classification of privacy and security issues in IoMT. We explore the issue from four distinct angles: privacy protection technologies, IoMT architecture, data type variations, and emerging technologies, with each section delving into more detailed analysis. Specifically, privacy protection technologies include cryptography method, Federated Learning (FL), blockchain technology and Differential Privacy (DP). We provide a more detailed classification for popular technologies such as blockchain. Regarding the IoMT architecture, we dissect the attack and defense mechanisms at the perception layer, the transmission and cloud layer, and the application layer. In terms of data types, we discuss the research on privacy protection based on the structural differences of various medical data. In the aspect of emerging technologies, we investigated the impact of four emerging technologies on privacy protection of IoMT. Our survey highlights the comprehensive and novel perspective of the characteristics of IoMT privacy protection, which will promote possible booming research in the future.