A Fast Secure Outsourcing of Ridge Regression Based on Singular-Value Decomposition
Shuyi Zhang, Shiran Pan, Wei Wang, Qiongxiao Wang · 2018
In modern science and engineering practices, regression analysis is widely used to deal with large-scale and sensitive data, such as personal credit information. When executing such tasks, it is preferable for a resource-constrained client, like a mobile phone, to outsource part of its expensive computation. However, it is challenging to outsource the computation to an untrusted cloud server without leak of sensitive information. In this paper, we propose a fast ridge regression outsourcing scheme FaSORR with a series of disguise techniques. Our scheme is based on singular-value decomposition(SVD), which is non-iterative and highly efficient with light workload at the client side. Moreover, we update the perturbation method and offer more protection to the original data than previous works. Experiment results show that the computing load at the client side decreases dramatically after the adoption of FaSORR. We analyze the performance of our scheme and compare it with previous works.