MedVault: A Blockchain-Integrated Deep Learning Architecture for Secure Medical Data Management

Abstract

Secure and efficient healthcare data sharing is critical for modern medical ecosystems, yet existing systems often suffer from limited scalability, privacy risks, and lack of intelligent data management. This study proposes MedVault, a hybrid blockchain-cloud-AI framework designed for secure, patient-centric healthcare data management. The architecture employs Corda for on-chain storage of consent records, metadata, and audit logs, while large medical datasets are encrypted and stored off-chain in AWS S3 with PostgreSQL metadata management. Security is reinforced using AES-256 encryption, Proxy Re-Encryption (PRE), Zero-Knowledge Proofs (ZKP), and decentralized identity management via Hyperledger Indy and Aries, while a FHIR-based gateway ensures seamless integration with electronic health records (EHRs). Intelligence is incorporated through deep learning models, including Autoencoders for anomaly detection, CNNLSTM for medical data analytics, Graph Neural Networks (GNNs) for consent prediction, DNNs for risk assessment, and Federated Learning (FL) for privacy-preserving distributed model training. Variational Autoencoders (VAEs) generate synthetic datasets, and Explainable AI techniques (SHAP, LIME) ensure interpretability. Extensive evaluations demonstrate that Corda-MedVault outperforms Hyperledger Fabric, Ethereum, and traditional centralized approaches across metrics such as blockchain latency, throughput, auditability, off-chain storage efficiency, energy consumption, anomaly detection, and consent prediction. Overall, the proposed system provides a scalable, energy-efficient, privacypreserving, and intelligent platform for real-time healthcare data sharing, offering a robust solution for secure and compliant medical data management.