Secure Optimization Computation Outsourcing in Cloud Computing A Case Study of Linear Programming - 2015
Cloud computing permits an economically promising paradigm of computation outsourcing. But, how to safeguard customers confidential information processed and generated during the computation is turning into the key security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and personal LP parameters owned by the customer. The resulting flexibility permits us to explore appropriate security/potency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP drawback as a set of matrices/vectors, we develop efficient privacy-preserving downside transformation techniques, that allow customers to remodel the first LP into some random one while protecting sensitive input/output data. To validate the computation result, we further explore the elemental duality theorem of LP and derive the required and sufficient conditions that correct results must satisfy. Such result verification mechanism is terribly economical and incurs shut-to-zero additional price on each cloud server and customers. In depth security analysis and experiment results show the immediate practicability of our mechanism style.
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