Blockchain and Machine Learning Approaches to Enhancing Data Privacy and Securing Distributed Systems

Abstract

The expansion of critical applications using distributed systems has established significant worries about protection of data and security throughout networks. Conventional protective methods struggle to gain control of decentralized network environments because trust and control distribution occurs among multiple participants. This study investigates how blockchain technology with machine learning approaches creates better data protection and system defense in distributed environments. Through blockchain technology users gain an unchangeable open ledger system which maintains data purity and through machine learning they get sophisticated analytic methods for spotting irregularities and forecasting security threats coupled with optimal security enhancement. The research examines existing approaches followed by a framework description for blockchain integration and their dual effects on security protocol enhancement. The paper demonstrates how blockchain and machine learning combine to solve modern distributed system security requirements as well as privacy protection and safe sharing in financial and healthcare sectors and IoT systems through an extensive assessment. The last part examines both research obstacles and contractions for the developing sector of study.