The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an integrated blockchain and federated learning framework that enables privacy-preserving collaborative AI across healthcare institutions without centralized data pooling. The proposed approach combines federated distillation for heterogeneous model collaboration with dynamic differential privacy that adapts noise injection to data sensitivity levels. A novel threshold key-sharing protocol ensures decentralized access control, while a dual-layer Quorum blockchain establishes immutable audit trails for all data sharing transactions. Experimental evaluation on clinical datasets (Mortality Prediction and Clinical Deterioration from eICU-CRD) demonstrates that our framework maintains diagnostic accuracy within 3.6% of centralized approaches while reducing communication overhead by 71% and providing formal privacy guarantees. For Clinical Deterioration prediction, the framework achieves 96.9% absolute accuracy on the Clinical Deterioration task with FD-DP at $ϵ$ = 1.0, representing only 0.14% degradation from centralized performance. The solution supports HIPAA-aligned technical safeguards, mitigates inference and membership attacks, and enables secure cross-institutional data sharing with real-time auditability. This work establishes a new paradigm for privacy-preserving healthcare AI that balances data utility, regulatory requirements, and protection against emerging threats in distributed clinical environments.

Healthcare analytics involving sensitive patient data demand robust statistical safeguards alongside verifiable compliance mechanisms that allow regulators to independently reconstruct query operations, applied policies, and reported outcomes without accessing raw medical information.We present a governance-first, upgradeable on-chain framework that anchors complete request lifecycles to public blockchain using keccak256 cryptographic commitments. This approach delineates a process wherein researchers submit structured queries, a controller captures active policy states, and hospitals provide result digests. A comprehensive and immutable event trail for this entire process is maintained on the Ethereum Sepolia blockchain. External auditors can then reconstruct complete timelines and verify consistent binding between requests, policies, and outcomes. The proposed modular verifier interface, currently implemented via configurable MockVerifier, maintains stable Application Binary Interface compatibility for future production verifier integration while validating end-to-end governance. We demonstrate the framework’s practicality through a detailed diabetes prevalence analytics case study. We provide detailed gas consumption profiles, request latency measurements, and observable failure modes that demonstrate how governance rules translate into enforceable reversions. The architecture maintains minimal on-chain state with Layer-2 readiness, offering immediate regulatory accountability while preserving straightforward upgrade paths to advanced cryptographic components like zk verifiers (Groth16), verifiable differential privacy through VRF-seeded randomness, content-addressed artifact storage, and versioned policy management. This approach effectively separates initial deployment feasibility from computationally intensive cryptography while delivering immediately actionable, externally verifiable evidence of policy compliance.