Conditional Entropy with Swarm Optimization Approach for Privacy Preservation of Datasets in Cloud

Background/Objective: The primary intension is to provide utility trade off and good privacy for intermediate datasets in cloud. Methods: An efficient conditional entropy and database difference ratio is employed for the process. Utility is taken care with the employment of conditional entropy with the help of Swarm Optimization (PSO). Privacy handled by database difference ratio. Findings: Conditional entropy is found out between the first column and the original database and this is taken as the fitness function in Particle Swarm Optimization (PSO). Database difference is taken between the original database and convoluted database in the consequent module to yield right selection data and tuple information from the intermediate datasets in cloud. Applications/Improvements: Improved methods are required to provide privacy and utility for the right selection of datasets. This approach has better results by having higher entropy values and lower dataset difference ratio.