A review on privacy protection in the internet of things using machine learning-based solutions. (24th November 2021)

• Record Type: Journal Article
• Authors: Pagale, Madhuri; Purohit, Richa
• Is Part Of: International journal of information privacy, security and integrity. Volume 5:Number 1(2021)
• Page Start: 69
• Page End: 91
• Publication Date: 2021-11-24
• Subjects: internet of things -- IoT -- survey -- privacy protection -- security -- ML -- attacks -- supervised learning -- unsupervised learning -- ML and blockchain -- privacy preservation -- sensors -- data security; Computer security -- Periodicals; Computer networks -- Access control -- Periodicals; Information technology -- Security measures -- Periodicals; Data protection -- Periodicals; Privacy, Right of -- Periodicals; 005.805
• Languages: English
• ISSNs: 1741-8496

Abstract

The internet of things (IoT) connects everyone in the world by offering a variety of applications and improving quality. In this technology, data protection is important. In recent years, many studies have been carried out on scalability, interoperability and resource constraints, such as the computational and energy strategies that machine learning (ML) uses to solve many data protection problems. This paper reviews these studies and aims to explore the possibilities and challenges of using data in ML-based solutions for IoT privacy. First, we explore and introduce various data sources into the IoT, and then classify them. We are also reviewing it and developing solutions developed based on existing ML and IoT to protect privacy. Finally, we'll look at how much specific data is used for privacy through ML-based solutions, and look at how these data sources can be used in the IoT ecosystem.