TITLE:
A Study on Differential Private Online Learning
AUTHORS:
Weilin Nie, Cheng Wang
KEYWORDS:
Online Learning, Differential Privacy, Output Perturbation, Error Decomposition, Learning Rate
JOURNAL NAME:
Journal of Computer and Communications,
Vol.5 No.2,
February
21,
2017
ABSTRACT: Online learning algorithms are very attractive, in which iterations are applied efficiently instead of solving some optimization problems. In this paper, online learning with protecting privacy is considered. A perturbation term is added into the classical online algorithms to obtain the differential privacy property. Firstly the distribution for the perturbation term is deduced, and then an error analysis for the new algorithms is performed, which shows the convergence and learning rate. From the error analysis, a choice for the parameters for differential privacy can be found theoretically.