Highly Efficient Linear Regression Outsourcing to a Cloud

With cloud computing and mobile computing becoming more and more popular, there are a lot potential applications for computation outsourcing to the cloud. This paper investigates the linear regression outsourcing problem, which is a quite common engineering task and employed in various applications, as a case study to find out the possible problems that need to be solved. We propose two protocols which can enable secure and efficient outsourcing of linear regression problems to the cloud. The protocols can protect the client’s data privacy well and at the same time have good efficiency. We show all subtleties and the techniques in designing such protocols. The main idea to protect the privacy is employing some transformations to the original linear regression problem to get a new problem which is sent to the cloud; and then transforming the answer returned back from the cloud to get the true solution to the original problem. Experimental results validate the practical usability of our protocols.

1 Introduction

With the advent of the age of cloud computing and mobile computing, computation outsourcing is becoming a popular computing paradigm. Clients with low computation power or resources can off-load their computation tasks to clouds, which are equipped with more computing resources. The clients could be mobile phones, companies, and senor nodes in a wireless sensor network, etc. Since there are a lot of such applications, this paper investigates the computation outsourcing problem with a case study of the linear regression (LR) task.