Privacy Protection and Performance Enhancement in IoT Applications using Blockchain and Machine Learning Techniques

Abstract

The Internet of Things (IoT) is producing significant amounts of data. Protecting this data from adversaries is therefore a prominent field of research. This paper conducts a review of the current state-of-the-art in the field of IoT integrated with Blockchain (BC) and Machine Learning (ML). The review focuses on the use of privacy and performance metrics to evaluate the effectiveness of these latest solutions. We first provide an overview of related works to have an understanding of what has been done. Then, we present five important privacy and performance metrics that have been used in the review. We then provide a detailed evaluation of state-of-the-art solutions. Finally, we identify and present open problems that need to be addressed in the form of future research directions. This paper provides valuable insights for researchers interested in improving privacy and performance in IoT applications, and opportunities for future research.