Jain, P. and Thakurta, A.G. (2014) Dimension Independent Risk Bounds for Differentially Private Learning. Proceedings of the 31st International Conference on Machine Learning, Beijing, 21-26 June 2014, 476-484.

has been cited by the following article:

• 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.