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Secure Healthcare Data Sharing Using Federated Learning, Blockchain, and Quantum Cryptography
Abstract
Secure exchange of patient healthcare data is vital due to the rise of AI in the medical field. However, this advancement introduces challenges such as data breaches, privacy violations, and regulatory demands. Traditional centralized systems store all data in one location, increasing cyberattack risks. This study proposes a secure framework integrating Federated Learning, Blockchain, and Quantum Cryptography. Federated Learning enables decentralized model training without sharing raw data, preserving patient privacy. Blockchain ensures data integrity using an immutable distributed ledger. Quantum Key Distribution (QKD) and AES-256 encryption protect data during transmission and storage. Files are stored in the InterPlanetary File System (IPFS), and their unique Content Identifiers (CIDs) are recorded on the blockchain for tamper-proof verification. Only users with valid quantum-generated keys can decrypt and access the data, ensuring strong privacy and security.
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