This document discusses a research on efficiently outsourcing the processing of massive datasets to the cloud while maintaining data privacy and security. It begins by introducing the growth of big data and challenges of analyzing large datasets. It then discusses how convex separable programming problems are involved in various applications and how solving them at large scales is computationally difficult for individual users. The proposed system develops an efficient transformation scheme to privately transform vectors and matrices before outsourcing computations to the cloud. It also uses convex separable programming to approximate convex functions with linear programs, which are then securely solved in the cloud. A verification scheme is used to validate the correctness of the cloud's returned results.
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