Privacy Preserving Cost Reducing Heuristic Approach For Intermediate Datasets In Cloud
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).
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Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate datasets will be generated and often stored them to save the cost of recomputing them. In this paper, toward achieving the minimum cost benchmark and for cost effectively storing large volume of generated application datasets in the cloud, we propose a novel highly cost effective and practical storage strategy that can automatically decide whether a generated dataset should be stored or not at runtime in the cloud and from that stored dataset, inorder to provide security to the sensitive dataset, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved and also the privacy requirements of data holders can be satisfied.
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Datasets storage, computation, cloud computing, data storage privacy, privacy preserving, intermediate dataset, privacy upper bound.