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The increasing massive data generated by various sources has given birth tobig data analytics. Solving large-scale nonlinear programming problems (NLPs)is one important big data analytics task that has applications in many domainssuch as transport and logistics. However, NLPs are usually too computationallyexpensive for resource-constrained users. Fortunately, cloud computing providesan alternative and economical service for resource-constrained users tooutsource their computation tasks to the cloud. However, one major concern withoutsourcing NLPs is the leakage of user's private information contained in NLPformulations and results. Although much work has been done onprivacy-preserving outsourcing of computation tasks, little attention has beenpaid to NLPs. In this paper, we for the first time investigate secureoutsourcing of general large-scale NLPs with nonlinear constraints. A secureand efficient transformation scheme at the user side is proposed to protectuser's private information; at the cloud side, generalized reduced gradientmethod is applied to effectively solve the transformed large-scale NLPs. Theproposed protocol is implemented on a cloud computing testbed. Experimentalevaluations demonstrate that significant time can be saved for users and theproposed mechanism has the potential for practical use.

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud