MedVault: A Blockchain-Integrated Deep Learning Architecture for Secure Medical Data Management
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".