Blockchain-Based Federated Learning Technique for Privacy Preservation and Security of Smart Electronic Health Records

This study introduces a blockchain-based lightweight encryption strategy with federated learning to address the scalability and trust concerns of electronic health records (EHR). After implementing lightweight encryption, the EHR data is stored in a decentralized cloud system. The importance of protecting the privacy and security of distant patients’ health records is explored. Now that stakeholders have a secure portal and cloud data is inaccessible, assaults on electronic healthcare records should decrease. The study guarantees full encryption throughout the whole conversation with federated learning. Deprived of the essential for a trusted third party, the system sets up active smart contracts at runtime between the sensor and the data user to facilitate the transfer of EHR data. To ensure that the data is private between the owner and user during the contract’s execution, it employs a very effective proxy re-encryption mechanism with federated learning. To examine the performance of the proposed system, it has been built and deployed on an Ethereum-based testbed. It is observed that the PSNR and MSE of the proposed model are 39 (<inline-formula> <tex-math notation="LaTeX">$1.07\times $ </tex-math></inline-formula>) and 229.6 (<inline-formula> <tex-math notation="LaTeX">$1.02\times$ </tex-math></inline-formula>) respectively. The entropy of the image is assessed to be 7.8 for the proposed model. This is also compared with existing algorithms and proved to be a secured model.