Author(s)

Weilin Nie, Cheng Wang

Huizhou University, Huizhou, China.

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.

Differential Privacy, Least Squares Regularization, Concentration Inequality, Error Decomposition

Nie, W. and Wang, C. (2017) Error Analysis and Variable Selection for Differential Private Learning Algorithm. Journal of Applied Mathematics and Physics, 5, 900-911. doi: 10.4236/jamp.2017.54079.