Outsourcing large-scale quadratic programming to a public cloud

Abstract

Cloud computing provides service for resource-constrained customers to perform large-scale scientific computation. However, it also brings some new challenges, which have to be considered in designing outsourcing protocols. In recent years, a few outsourcing protocols have been proposed for different kinds of problems. Quadratic programming (QP) is a class of mathematical optimization problem, and solving a large-scale QP problem requires a large amount of computation. Thus, there is a great need for customer to outsource large-scale QP problem to cloud. In this paper, we design a secure, verifiable, and efficient outsourcing protocol for QP problem. For security consideration, we encrypt the matrices and vectors contained in the QP problem in an efficient way. After cloud computing, we decrypt the result to get the ultimate solution. To ensure correctness, we verify the result returned by the cloud through Karush-Kuhn-Tucker conditions that are the necessary and sufficient conditions for the optimal solution. We also present complexity analysis and numerical simulations to illustrate the efficiency of our outsourcing protocol.