Abstract
Cloud service providers emphasize the concept of virtual machines, wherein resources and services are accessed over a distributed network system. The loss of user privacy is indeed contempt in concern to any individual or organization, particularly concerning the Healthcare infrastructure. This research paper introduces a Euclidean L3P-based Multi-Objective Successive Approximation (EMSA) algorithm, an efficient measure of privacy in the Healthcare Cloud. The role-based encryption keys are the critical foundation for the storage of sensitive data in cloud environments is presented here. The proposed EMSA algorithm is the hybridization of the existing Euclidean L3P Distance algorithm, Multi-objective Optimization algorithm, and Successive Approximation Iterative Proximate algorithm. In addition, the performance of the proposed EMSA compared with Bat, PUBAT, TPNGS, WOA, and CIC-WOA algorithms based on performance metrics, such as fitness, privacy, and utility. The simulation shows that, comparison to the existing state-of-the-art algorithms, the proposed EMSA model achieves higher privacy values of 0.34, 0.42, 0.42, 0.35, and 0.30.
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