Enhancing Privacy and Trust in Machine Learning through Blockchain Technology

The incorporation of blockchain technology has emerged as a promising approach to address these crucial difficulties in an era marked by the broad deployment of machine learning (ML) and the growing concern for data privacy and trust. This study examines the ways in which blockchain technology and machine learning might work together to improve privacy and trust in data-driven applications. The first part of the paper explores the current state of privacy and trust issues in machine learning, emphasizing the risks connected to centralized data processing and storage. The core ideas of blockchain technology are then explained, with special emphasis on its decentralized and unchangeable ledger structure. These ideas are mentioned as the cornerstone for creating tamper-proof, transparent, and safe systems for machine learning applications. Through an extensive literature study, the paper examines several methods and strategies for incorporating blockchain technology into machine learning workflows. These include consensus-based methods for updating models, safe data exchange via decentralized networks, and methods for protecting privacy such homomorphic encryption. The useful applications of this integration are illustrated through discussions of real-world use cases from industries including healthcare, banking, and IoT. The study also looks at the trade-offs and difficulties of integrating blockchain with machine learning, highlighting the significance of interoperability, scalability, and energy efficiency. It also covers the necessity for standardization in this developing industry, as well as challenges related to compliance and regulations. In summary, the paper highlights how blockchain technology has the ability to transform machine learning by establishing a new era of trust and data protection.