Secure and Privacy Preserving Data Mining and Aggregation in IoT Applications
Vashi Dhankar, Anu Rathee · 2020
This chapter looks at cryptographic techniques, data slicing methods, and evolutionary methods to obtain privacy preserving data aggregation. Data mining is done one step at a time and each part of the process requires its own method of security and privacy preservation. In response, service providers have to provide secure private data aggregation schemes. The chapter discusses the ways to ensure security and privacy for Internet of Things (IoT) applications, and some of the challenges to security and privacy. Since the process of data mining has many steps, there are different privacy preservation schemes and paradigms on each level of data mining. The output of data mining techniques is often made accessible through applications or interfaces. Finally, hybrid approaches such as evolutionary game-based secure private data aggregation guide us into future work. These applications help to maintain security and privacy when an IoT application interacts with non-IoT based wired or ad-hoc networks.