This paper addresses the escalating challenges of data privacy and security in healthcare analytics, particularly as medical institutions increasingly depend on big data and collaborative research. PrivaHealth FL, a comprehensive system that merges Federated Learning (FL) with Blockchain technology, is introduced to facilitate secure, privacy-preserving analysis of distributed medical data. Unlike prior frameworks that address individual security concerns in isolation, PrivaHealth FL integrates three orthogonal defense mechanisms: Differential Privacy (DP) using the Gaussian Mechanism with adjustable privacy budget ε ∈ [0.1, 2.0]; Homomorphic Encryption (HE) to protect gradient confidentiality during aggregation; and Byzantine Fault Tolerance (BFT) via a Krum-style distance filter to neutralize malicious participant updates. The system is built on a custom blockchain ledger providing full auditability through SHA-256 linked blocks and ECDSA digital signatures. Experimental evaluation across five simulated hospitals demonstrates that PrivaHealth FL achieves 86.8% global accuracy after 20 federated rounds — only 3.3% below an unprotected baseline — while reducing membership inference attack success rates by 87.3% at ε = 0.5. Byzantine fault detection achieves a 100% true positive rate with 0% false positives. A formal Threat Model analysis covering seven attack vectors confirms that PrivaHealth FL provides comprehensive, multi-layered protection suitable for real-world clinical deployment.

Federated Learning Blockchain Differential Privacy Homomorphic Encryption Byzantine Fault Tolerance Healthcare Data Analytics Privacy-Preserving Machine Learning.