An efficient and secure ridge regression outsourcing scheme in wearable devices

Abstract 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 privacy 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 cost 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.