A secure data protection technique for healthcare data in the cloud using homomorphic encryption and Jaya–Whale optimization algorithm

Abstract

Cloud computing has become a powerful mechanism for initiating secure communication among users. The advancements in the technology lead to provide various services, like accessing network, resources, and platform. However, handling large datasets and security are major issues in the cloud systems. Hence, this paper proposes a technique, namely, Jaya–Whale Optimization (JWO), which is the integration of Jaya algorithm and Whale optimization algorithm (WOA) and adapts homomorphic encryption for initiating secure data transmission in the cloud. The original data are preserved by generating Data Protection (DP) coefficient using the proposed JWO algorithm. In the proposed algorithm, the fitness is calculated based on privacy and utility parameters for selecting the optimal solution. Also, the sanitized data are generated by EXORing the Key Information Product (KIP) matrix and key vector. Finally, the data owner provides the key to the users for retrieving the original data from the sanitized data. The experimentation is carried out using Cleveland, Hungarian, and Switzerland datasets in terms of BD, accuracy, and fitness and the analysis shows that the proposed JWO provides superior performance in terms of BD, accuracy, and fitness parameters with values 0.720, 0.822, and 0.722.

An Augmented Salp-swarm Optimization Based on Paillier Federated Multi-layer Perceptron (Pf-mlp) and Homomorphic Encryption Standard (Hes) Techniques for Data Security in Cloud Systems

“…The most important thing that this work did was come up with a new way to protect the privacy and security of cloud storage systems. Sudha et al [18] deployed a Jaya-Whale optimization technique for protecting healthcare data in cloud systems.…”