Privacy Aware Learning
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 17, 2014
- Source ID
- 10.1145/2666468
Entities
People
- John Duchi
- Martin J. Wainwright
- Michael I. Jordan
Organizations
- United States Army Research Laboratory
- University of California, Berkeley