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2016 ICML ICML 2016

Differentially Private Policy Evaluation

Abstract

We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Reinforcement Learning and Security & Privacy
📈 Trend Setter - Privacy
🐣 Hot Topic Early Bird - reinforcement learning

Authors

Borja Balle, Maziar Gomrokchi, Doina Precup

Topics

Machine Learning > Application Areas > Privacy Reinforcement Learning > Methods > Policy Learning Reinforcement Learning > Applications > Value Iteration Security & Privacy > Privacy

Keywords

privacy-preserving machine learning differential privacy reinforcement learning policy evaluation private algorithm
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