Blockchain Approaches for Privacy Preservation: A Review

Abstract

A Blockchain is a trustworthy, immutable, decentralized database system that functions in different nodes. During the age of decentralized networks and large datasets, the confluence of Blockchain with Intelligent Machine technologies nowadays emerged as an excellent solution to provide robust, transparent, & private transactions. While the individual merits of Blockchain in ensuring data integrity and ML in deriving insights are well-established, their synergistic effects particularly in the realm of privacy preservation are yet to be fully explored. This fusion has the potential to revolutionize sectors like health care, finance, and supply chain by offering unprecedented levels of data privacy without compromising on system performance. This paper presents a comprehensive review of existing models that employ machine learning techniques for privacy preservation operations within blockchain frameworks. The paper contributes to the academic discourse by laying down a foundational framework for understanding and selecting the most appropriate blockchain-ML models for privacy preservation. The insights derived from this work are instrumental in steering the future development and deployment of secure, efficient, and privacy-preserving solutions across various industry verticals & scenarios.