Privacy-preserving Distributed Calculation Methods of a Least-squares Estimator for Linear Regression Models

Abstract

In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.