Privacy Aware Learning

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...

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Bibliographic Details
Published in:Journal of the ACM 2014-11, Vol.61 (6), p.1-57
Main Authors: Duchi, John C, Jordan, Michael I, Wainwright, Martin J
Format: Article
Language:English
Subjects:
Confidentiality
Convergence
Estimating techniques
Estimators
Learning
Lower bounds
Optimization
Preserves
Privacy
Risk management
Studies
Tradeoff analysis
Utilities
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Summary: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.
ISSN:0004-5411
1557-735X
DOI:10.1145/2666468