Implementation and Mathematical Modelling of Data Sharing and Privacy Preserving Technique in a Blockchain Network by Using Machine Learning Algorithms

Blockchain technology has emerged as a reliable and secure decentralized network with multifaceted applications in banking, finance, insurance, healthcare, and business domains. Recent trends within blockchain communities indicate a growing interest in deploying machine learning models to extract valuable insights from extensive, geographically dispersed datasets owned by individual participants. To enable learning models without centralized data repositories, extensive research has focused on developing machine learning algorithms tailored for blockchain networks. However, despite numerous proposals, privacy and security concerns remain inadequately addressed, revealing vulnerabilities in architecture and operational efficiency limitations. The proposed ensemble machine learning model presents a pioneering solution, aiming to systematically resolve privacy, security, and performance issues within blockchain systems. The novelty of this approach lies in its targeted resolution of critical challenges at the intersection of blockchain technology and machine learning. While prior research has delved into integrating machine learning into blockchain networks, this model stands out by introducing a privacy-centric methodology that systematically addresses the core issues of privacy, security, and performance. Moreover, the proposed model promises enhanced resilience against adversarial attacks compared to other aggregation rules within a differentially private scenario. The innovative ensemble machine learning model for blockchain exhibits a significant 20% improvement in data privacy, a 15% boost in security, and a notable 25% enhancement in operational performance.