Electronic Health Record (EHR) systems require secure, privacy-preserving and auditable access control mechanisms to protect sensitive medical data while ensuring timely availability in clinical environments. This paper proposes a privacy-preserving biometric access control framework that integrates cancellable biometric authentication with a permissioned blockchain architecture. Biometric templates are protected using fuzzy extractor–based transformation to prevent inversion and replay attacks, while attribute-based access control with emergency override support enables context-aware authorization. Encrypted medical records are stored off-chain and immutable access metadata and consent states are maintained on-chain to ensure integrity, transparency and traceability. Quantitative evaluation was conducted using the Labeled Faces in the Wild (LFW) and FVC2004 benchmark datasets, achieving Equal Error Rates of 1.65% for facial authentication and 1.1% for fingerprint verification. Blockchain performance analysis demonstrates a median transaction latency of approximately 420 ms and stable throughput under moderate load conditions. The framework further incorporates credential revocation, key rotation and smart contract security validation to strengthen lifecycle governance. The results indicate that the proposed approach provides secure, auditable and scalable EHR access control while maintaining strong authentication performance and operational feasibility in institutional healthcare environments.
B. Sindhu (Sun,) studied this question.