Practical Privacy-Preserving Outsourcing of Large-Scale Matrix Determinant Computation in the Cloud

Abstract

Large-Scale matrix determinant computation (LMDC) is a common scientific and engineering computational task and has a number of applications. But such computation involves enormous computing resources, which is burdensome for the clients. Cloud computing enables computational resource-constrained clients to economically outsource such computations to the cloud server. In this paper, we investigate the privacy-preserving large-scale matrix determinant computation outsourcing problem, where the clients can outsource LMDC to the untrusted cloud server, relieving the clients from computation burden. We propose a new privacy-preserving algorithm for outsourcing LMDC, which substantially reduces the computation burden on the client side. Our algorithm builds on a series of carefully-designed pseudorandom matrices, which can hide the original matrix from the cloud server with low computational complexity. The extensive security analysis shows that our algorithm is practically-secure, and offers a higher level of privacy protection than the state-of-the-art on LMDC outsourcing. We provide extensive theoretical analysis and experimental evaluation to show its high-efficiency and security compared to the previous works.