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

TL;DR

This paper introduces a secure outsourcing scheme for large-scale nonlinear programming problems to the cloud, protecting user privacy while enabling efficient computation and demonstrating practical feasibility.

Contribution

It proposes a novel privacy-preserving transformation scheme for outsourcing NLPs and applies a generalized reduced gradient method for efficient cloud-based solving.

Findings

Significant time savings for users in outsourcing NLPs.

Effective privacy protection of user data during outsourcing.

Practical implementation on a cloud testbed shows feasibility.

Abstract

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.