TITLE:
Error Analysis and Variable Selection for Differential Private Learning Algorithm
AUTHORS:
Weilin Nie, Cheng Wang
KEYWORDS:
Differential Privacy, Least Squares Regularization, Concentration Inequality, Error Decomposition
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.5 No.4,
April
30,
2017
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.