An enhanced bacterial foraging optimization algorithm for secure data storage and privacy-preserving in cloud

Cloud fle access is the most widely used peer-to-peer (P2P) application, in which users share their data and other users can
access it via P2P networks. The need for security in the cloud system grows day by day, as organizations collect a massive
amount of users' confdential information. Both the outsourced data and the unprotected user's sensitive data need to be
protected under the cloud security claims since the advanced P2P networks are prone to damage. The recurring security
breach in the cloud necessitates the establishment of an advanced legal data protection strategy. Various researchers have
attempted to develop privacy-preserving cloud computing systems employing Artifcial Intelligence (AI) techniques, however, they have not been successful in achieving optimal privacy. AI approaches implemented in the cloud assist applications in efcient data management by analyzing, updating, classifying, and providing users with real-time decision-making support. AI approaches can also detect fraudulent activity by analyzing deviations in normal data patterns entering the system. To handle the security concerns in the cloud, this paper presents a novel cybersecurity architecture using the Chaotic chemotaxis and Gaussian mutation-based Bacterial Foraging Optimization with a genetic crossover operation (CGBFO-GC) algorithm. The CGBF0-GC algorithm cleanses and restores the data using a multiobjective optimal key generation mechanism based on the following constraints: data preservation, modifcation, and hiding ratio. The simulation results show that the proposed methodology outperforms existing methods in terms of convergence, key sensitivity analysis, and resistance to known and chosen-plaintext attacks.