Enabling Privacy-Preserving Parallel Computation of Linear Regression in Edge Computing Networks

Abstract

Linear regression is a classical statistical model with a wide range of applications. The function of linear regression is to predict the value of a dependent variable (the output) given an independent variable (the input). The training of a linear regression model is to find a linear relationship between the input and the output based on data samples. IoT applications usually require real-time data processing. Nonetheless, the existing schemes about privacy-preserving outsourcing of linear regression cannot fully meet the rapid response requirement for computation. To address this issue, we consider employing multiple edge servers to accomplish privacy-preserving parallel computation of linear regression. We propose two novel solutions based on edge servers in edge computing networks and construct two efficient schemes for linear regression. In the first scheme, we present a new blinding technique for data privacy protection. Two edge servers are employed to execute the encrypted linear regression task in parallel. To further enhance the efficiency, we design an adaptive parallel algorithm, which is adopted in the second scheme. Multiple edge servers are employed in the second scheme to achieve higher efficiency. We analyze the correctness, privacy, and verifiability of the proposed schemes. Finally, we assess the computational overhead of the proposed schemes and conduct experiments to validate the performance advantages of the proposed schemes.