Privacy Aware Learning

This work establishes sharp upper and lower bounds on the convergence rates of statistical estimation procedures in a local privacy framework and exhibits a precise tradeoff between the amount of privacy the data preserves and the utility of any statistical estimator or learning procedure.

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.