Amrutha Mary, J.Arthi Jaya Kumari


Within this paper, we advise a manuscript upper bound privacy leakage constraint-based method of identify which intermediate data sets have to be encoded and that do not, to ensure that privacy-protecting cost could be saved as the privacy needs of information holders can nonetheless be satisfied. To be able to curtail the general expenses by staying away from frequent computation to acquire these data sets. Such situations are very common because data customers frequently reanalyze results, conduct new analysis on intermediate data sets, or share some intermediate results with other people for collaboration. Across the processing of these programs, a sizable amount of intermediate data sets is going to be produced, and frequently stored in order to save the price of computing them. Cloud computing provides massive computation power and storage capacity which enable customers to deploy computation and knowledge-intensive programs without infrastructure investment. However, protecting the privacy of intermediate data sets turns into a challenging problem because opponents may recover privacy-sensitive information by examining multiple intermediate data sets. Evaluation results show the privacy-protecting price of intermediate data sets could be considerably reduced with this approach over existing ones where all data sets are encoded. Encrypting ALL data takes hold cloud is broadly adopted in existing methods to address this concern. But we reason that encrypting all intermediate data sets are neither efficient nor cost-effective since it is very time intensive and pricey for data-intensive programs to en/decrypt data sets frequently while carrying out any operation in it. Finally, we design an operating heuristic formula accordingly to recognize the information sets that should be encoded.


Data Storage Privacy; Intermediate Data Set; Privacy Upper Bound.


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