DECENTRALIZED LEARNING FOR HEALTHCARE DATA USING HOMOMORPHIC ENCRYPTION AND BLOCKCHAIN

Gasi Lakshmi Praveena

Abstract


Healthcare data analytics faces a core challenge: building accurate predictive models without exposing sensitive patient records. This work presents a secure framework that integrates federated learning, homomorphic encryption, and blockchain to address this problem in electronic health record analysis. Patient data is encrypted using the CKKS scheme, allowing model training directly on encrypted values without revealing raw information. Multiple clients train local models independently and share only encrypted model weights. A blockchain layer is used to store and verify these weights, ensuring tamper resistance, traceability, and secure aggregation. The global model is constructed from blockchain-stored weights, preserving data privacy while enabling collaborative learning. The framework is evaluated using an XGBoost classifier on a Chronic Kidney Disease EHR dataset. Experimental results show that local models achieve high accuracy, while the global federated model maintains competitive performance with enhanced security guarantees. Although aggregation increases computation time, the approach significantly strengthens data confidentiality and model integrity, making it suitable for privacy-critical healthcare applications.

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