Using Machine Learning to Deal with Privacy and Confidentiality in Internet of Things: An Overview

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors, from healthcare and smart cities to industrial automation, by enabling seamless data collection and communication. However, this interconnectedness raises critical concerns about privacy and confidentiality, as sensitive data is continuously monitored, transmitted, and stored. This chapter examines the pivotal role of machine learning (ML) in addressing these challenges, offering sophisticated tools and methods to enhance data protection mechanisms and mitigate privacy risks. It explores key ML techniques such as differential privacy, federated learning, homomorphic encryption, and adversarial machine learning, each providing unique capabilities for preserving privacy while enabling valuable data analysis. The chapter also delves into the intricate privacy issues and challenges associated with data collection, storage, transmission, and access control in IoT networks. It highlights the importance of secure communication protocols, data integrity, and robust access control mechanisms in safeguarding sensitive information. Furthermore, the chapter discusses the confidentiality concerns in IoT data handling, emphasizing the need for strong encryption, compliance with privacy regulations, and the preservation of individual autonomy. By providing a comprehensive overview of the challenges and opportunities in tackling privacy and confidentiality issues in IoT through ML, this chapter offers valuable insights into the future of secure and privacy-preserving IoT ecosystems.