Blockchain-Enabled Secure and Optimized Healthcare Data Protection Using Fuzzy Embedded Recurrent Neural Network and Elliptic Galois Cryptography with Proof of Monitoring in Cloud Environments

Ensuring the security and privacy of patient data is vital in the healthcare industry, as medical professionals require access to accurate health information for precise diagnoses. In similar vein, patients often need access to their information. However, it is essential to make sure private information about health is transferred safely. In this manuscript, Blockchain-Enabled Secure and Optimized Healthcare Data Protection using Fuzzy Embedded Recurrent Neural Network and Elliptic Galois Cryptography with Proof of Monitoring in Cloud Environments (BSHDP-FERNN-EGC-CE) is proposed. The approach encodes medical data, encrypts it using Elliptic Galois Cryptography, and secures the encryption key in a blockchain using Proof-of-Monitoring for enhanced privacy. FERNN monitors encrypted data access in the cloud, identifying abnormal patterns effectively. Experimental results demonstrate 12.25%, 8.65%, and 13.27% higher accuracy and 7.49%, 10.47%, and 13.55% lower encryption time compared to SPHD-ECDNN-BCE, BODL-BDSDT-HCS, and BACS-DNN-HCA, confirming superior efficiency and robust data protection.