Deep Learning with Blockchain Technology for Secure Data Management in Healthcare Sector using Hybrid Elliptic Curve-Rivest–Shamir–Adleman Cryptography

Abstract

Healthcare management, it involves various processes such as managing finance, logistics, inventory, staff, and patients. Nowadays, a healthcare system also faces many challenges regarding trust, privacy, security, traceability, immutability, and so forth. Here, the traditional data collection and processing of Electronic Health Records is a centralized technique that faces several risks that may cause system failure and affect the system’s reliability. This article aims to develop secured data management in the healthcare sector using blockchain technology and deep learning. Here, the data is collected in the patient node and encrypting the data by Hybrid Elliptic Curve-Rivest–Shamir–Adleman Cryptography (H-EC-RSAC). The institution nodes handle the data for analysis, in which the concept of an improved deep neural network (DNN) is utilized. In addition, the blockchain sector stores the index and address of health data. The modification is done in both H-EC-RSAC and improved DNN by a hybrid meta-heuristic algorithm called adaptive mixture ratio-based Spider Monkey–Cat Swarm Optimization (AM-SMCSO) for enhancing encryption and authentication performance. Throughout the analysis, the accuracy of the AM-SMCSO-H-EC-RSAC + DNN attains 93.8%. The precision of the designed method achieves 93.2%. The developed approach proves its ability to offer maximum security to healthcare data.