Machine Learning Aspects for Trustworthy Internet of Healthcare Things
Pradeep Bedi, S. B. Goyal, Jugnesh Kumar, Preetishree Patnaik · 2022
With the rapid development of communication tools and techniques as well as intelligent processing and analysis had led to the development of smart Internet of Things (IoT) applications. The IoT application generates a huge amount of data every day as its application is vast. One of the most important applications of IoT is in the healthcare sector that generates large and sensitive data. So, to process such a large amount of data over an insecure network is an issue of concern. In this chapter, an overview of IoT architecture, issues, and solutions are discussed. The existing privacy-preserving inference approaches have problems such as high computation and communication overheads. In this chapter an overview of security challenges and security models using machine learning for the Internet of Healthcare Things (IoHT) is given along with a framework is proposed that can enforce security and trustworthiness on the Internet of Healthcare Things (IoHT) by amalgamating privacy and deep learning. This chapter will give a direction for future research work while designing a secure IoHT framework with low latency, and fast processing for accurate end-to-end data delivery.