AN ADAPTIVE ELLIPTICAL CURVE CRYPTOGRAPHY-RIVEST-SHAMIR-ADLEMAN-BASED ENCRYPTION FOR IoT HEALTHCARE SECURITY MODEL WITH BLOCKCHAIN TECHNOLOGY

Abstract

The healthcare systems follow the path of client-server architecture in a centralized manner for managing the patient’s health information with the secure storage process. Medical data can be preserved in every medical institution that remains safely in silos. It could be able to access without proper authorization and cannot be transferred. At the same time, while handling massive data, the clinics or hospitals are fragile enough to govern the patient’s health information as the system is confined with such security constraints. Rather than other methodologies, blockchain plays a pivotal part in the medical industry for storing medical data in a secure manner. Since the blockchain represents a reliable and scalable decentralized network, it can conquer certain challenges of classical methods. Furthermore, the Internet of Things (IoT) is an emerging process of fetching data by wearing some sensors and devices. Through these intelligent elements, the healthcare data can be shared and enriched with the data quality for effective healthcare services. Nevertheless, this connection becomes vulnerable to data privacy and security because the data management relies on open service networks. In order to overcome these effects, a novel IoT-derived blockchain model is proposed for storing and authenticating purposes. Initially, medical data are collected from different benchmark sources and subjected to the data encryption phase. Then, encryption is performed with the help of Adaptive Elliptical Curve Cryptography and Rivest-Shamir-Adleman (A-ECC-RSA), where the key optimization is done by utilizing Enhanced Sand Cat Swarm Optimization (ESCSO). Further, encrypted data are stored in the blockchain and finally authentication access is performed with the help of Adaptive Bidirectional Long Short-Term Memory (ABiLSTM) using biometric information, where the BiLSTM parameters are optimally selected by developed ESCSO. Thus, the developed model will attain an effective security rate than conventional models that can be established through several experiments on the proposed model.