A multi-objective privacy preservation model for cloud security using hybrid Jaya-based shark smell optimization

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security. (C) 2022 Published by Elsevier B.V. on behalf of King Saud University.