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2012 NIPS NeurIPS 2012

Privacy Aware Learning

Abstract

We study statistical risk minimization problems under a version of privacy in which the data is kept confidential even from the learner. In this local privacy framework, we show 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, measured by convergence rate, of any statistical estimator.

📈 Trend Setter — Privacy
🧭 Keyword Pioneer — privacy aware learning
🐣 Hot Topic Early Bird — differential privacy
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Security & Privacy

Authors

Martin J. Wainwright , Michael I. Jordan , John C. Duchi

Topics

Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Application Areas > Privacy Machine Learning > Learning Types > Supervised Learning Security & Privacy > Differential Privacy Security & Privacy > Privacy Artificial Intelligence > Core AI > Privacy Machine Learning > Learning Types > Privacy

Keywords

differential privacy privacy-preserving learning privacy preserving statistical estimation privacy aware learning statistical risk minimization local privacy privacy utility tradeoff convergence rate privacy-utility tradeoff
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