Blockchain-Enabled Privacy Protection in Machine Learning

Abstract

The integration of blockchain technology with machine learning (ML) holds promise in addressing privacy concerns in data-driven applications. Traditional ML models often require centralized data repositories, posing significant risks to data privacy and security. Blockchain's decentralized and immutable ledger offers a novel approach to enhancing privacy protection by enabling secure data sharing and model training without compromising individual data ownership. This paper explores various blockchain-based techniques such as distributed ledger storage, cryptographic hashing, and smart contracts to establish trust and transparency in ML processes. We discuss practical applications of blockchain in preserving privacy during data aggregation, model training, and inference stages, highlighting its potential to revolutionize data governance frameworks. Through case studies and theoretical analysis, we illustrate how blockchain can mitigate privacy risks while fostering collaborative ML development in a secure and ethical manner.