Pioneering Privacy in Healthcare Analytics with Federated Learning for Next-Generation Data Security Solutions

Abstract

This research explores the innovative integration of Blockchain technology with Federated Learning (FL) systems, aiming to enhance data privacy, security, and integrity in healthcare applications. Recognizing the critical need for privacy-preserving mechanisms in distributed learning environments, we propose a holistic framework that combines the decentralized, tamper-proof characteristics of Blockchain with the advanced privacy and security features of Differential Privacy (DP), Secure Multi-Party Computation (SMPC) using the SPDZ algorithm, and Homomorphic Encryption (HE). Our research systematically evaluates the efficacy of this integrated approach through extensive literature review and experimental analysis, focusing on key aspects such as data confidentiality, model accuracy, scalability, and computational efficiency. The results demonstrate a notable improvement in securing medical data against privacy threats and unauthorized access, while maintaining the collaborative learning capability of FL systems. By leveraging Blockchain's immutable ledger for model updates and employing cryptographic techniques for data protection, our framework establishes a new benchmark for privacy and transparency in healthcare federated learning. This paper not only highlights the practical applications and potential challenges of the proposed integration but also sets the stage for future exploration into optimizing these technologies for real-world deployments. The convergence of Blockchain with DP, SMPC, and HE within FL represents a significant step towards achieving secure, privacy-preserving, and efficient machine learning models, particularly in the sensitive domain of healthcare.