The internet of things (IoT) is a scalable network of interconnected smart devices that aims to improve quality of life, business growth, and efficiency across multiple sectors. Since the IoT is an expanding network, a large amount of data is generated, collected, and exchanged. However, most of this data is personal data that contains private or sensitive information, which makes it a target for several cyber threats due to poor encryption, weak authentication mechanisms, and insecure communications. Therefore, ensuring the privacy and confidentiality of sensitive information remains a critical challenge. This paper presents a comprehensive literature review focusing on privacy and confidentiality issues within the IoT ecosystem. It categorizes existing research into privacy-preserving techniques, authentication and trust mechanisms, and machine learning-based solutions. Beginning by detailing the review methodology employed to gather and analyze relevant research. The review then explores recent research work related to privacy concerns and authentication and trust mechanisms, emphasizing various approaches and solutions developed to address these challenges. The paper further delves into machine learning-based solutions that offer innovative methods for enhancing privacy and confidentiality.
Kandil et al. (Fri,) studied this question.