Error Analysis and Variable Selection for Differential Private Learning Algorithm

Abstract

In this paper, we construct a modified least squares regression algorithm which can provide privacy protection. A new concentration inequality is applied and the expected error bound is derived by error decomposition. Furthermore, via the error analysis, we find a method to choose an appropriate parameter  to balance the error and privacy.