← Back to papers
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.

📈 Trend Setter — Privacy
🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🐣 Hot Topic Early Bird — differential privacy
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
🧭 Keyword Pioneer — private algorithm

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
Download PDF

Related papers

Associative Long Short-Term Memory 2016
Recycling Randomness with Structure for Sublinear time Kernel Expansions 2016
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues 2016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization 2016
Hawkes Processes with Stochastic Excitations 2016