Recent trends towards privacy-preservation in Internet of Things, its challenges and future directions

Abstract

The Internet of Things (IoT) is a self-configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As ‘things’ are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT-based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning-based data minimisation method that, in these networks, can be very beneficial for privacy-preserving.