Privacy Aware Learning
Machine Learning
2013-10-11 v2 Information Theory
Machine Learning
math.IT
Abstract
We study statistical risk minimization problems under a privacy model in which the data is kept confidential even from the learner. In this local privacy framework, we establish sharp upper and lower bounds on the convergence rates of statistical estimation procedures. As a consequence, we exhibit a precise tradeoff between the amount of privacy the data preserves and the utility, as measured by convergence rate, of any statistical estimator or learning procedure.
Cite
@article{arxiv.1210.2085,
title = {Privacy Aware Learning},
author = {John C. Duchi and Michael I. Jordan and Martin J. Wainwright},
journal= {arXiv preprint arXiv:1210.2085},
year = {2013}
}
Comments
60 pages