An efficient and secure ridge regression outsourcing scheme in wearable devices

作者：Ma, Xinshu; Zhu, Youwen^*; Li, Xingxin

来源：Computers & Electrical Engineering, 2017, 63: 246-256.

DOI：10.1016/j.compeleceng.2017.07.019

• Cloud computing
• Ridge regression
• Secure outsourcing
• Wearable medical devices
• Privacy

Ridge regression is an important approach in many applications, such as healthcare system of smart wearable equipments. Due to the limited resources of wearable devices, outsourcing is a promising computation paradigm. Nevertheless, it also suffers from some privac challenges, as outsourced computation probably involves some sensitive data. In this paper we propose a ridge regression outsourcing scheme, which can securely utilize the cloud to analyse large-scale wearable device dataset and dramatically reduce the computation cos of the resource-limited clients. Technically, we use random vectors and dense matrices to perturb input dataset and regression output, such that both input privacy and output privacy can be efficiently protected. Then, we present a highly-efficient verification algorithm to robustly check the correctness of cloud's answer against a dishonest/lazy cloud server Finally, we evaluate our scheme through theoretical analysis and extensive experiments The results show we can achieve input/output privacy, correctness, robust checkability and practical efficiency.

• 出版日期2017-10
• 单位桂林电子科技大学; 南京航空航天大学