Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud

Abstract

Cloud computing provides enormous storage capacity and huge computation power. Cloud enables users to deploy data-intensive applications without substructure investment. Intermediate data are generated under the cloud applications and stored again in cloud. An opponent may analyse multiple intermediate data sets to access the privacy sensitive information.The proposed technique to have good privacy and utility trade off, joint entropy with adaptive optimization process is used to maintain the privacy in cloud. Here optimal entropy value process using adaptive particle swarm optimization (APSO) process. After get the optimal entropy database difference model was processed, Entropy and database difference ratio is taken as the evaluation matrices and is tested using various datasets. The technique is also compared to the existing PSO optimization process on privacy preservation in cloud. An adaption feature of Particle swarm optimization show that APSO enhances privacy and preserving cost of intermediate data sets can be significantly saved.