BLOCKCHAIN-ASSISTED PRIVACY PRESERVATION MODEL FOR ELECTRONIC HEALTHCARE DATA WITH OPTIMAL KEY GENERATION

In the contemporary era of healthcare informatics, preserving the privacy of EHD while ensuring its accessibility for legitimate purposes poses a significant challenge. Conventional methods of protecting sensitive medical information have frequently proven inadequate, exposing vulnerabilities that jeopardize patient privacy. Blockchain technology has emerged as a promising solution due to its inherent characteristics of decentralization, immutability, and transparency. The objective of this paper is to propose a novel Blockchain-Assisted Optimized Privacy Preservation Model (BAOPPM) for Electronic Health Records (EHRs) that enhances the privacy of sensitive healthcare data while ensuring its accessibility for legitimate purposes. The model leverages blockchain technology for secure data storage and integrates advanced algorithms for efficient privacy key generation and data sanitization. At first, the HCK Algorithm is used to generate sensitive data clusters and non-sensitive data clusters. Then, the sensitive data are sanitized by adopting the Kronecker product between the sensitive data and the optimal key. Here, the optimal key is generated via the BMACO algorithm that considers the level of privacy by including privacy and privacy information ratio. Finally, the sanitized data are stored in the blockchain. On the contrary, the data restoration process is done, which is the reverse operation of data sanitization to retrieve the sensitive data from the blockchain. The BMACO approach achieves the lowest cost rate of 0.2451 at the 50th iteration, showcasing its superior convergence efficiency. In contrast, conventional methods such as COOT, BMO, KOA, COATI, LBO, EHO-OBL, and ECSO register higher cost ratings of 0.2463, 0.2498, 0.2512, 0.2484, 0.2473, 0.2536, and 0.2469, respectively. The sanitized data were stored in the blockchain and restored securely using the developed model.