ICML 2016poster39 citations
Differentially Private Policy Evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup
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.
BibTeX
@InProceedings{pmlr-v48-balle16,
title = {Differentially Private Policy Evaluation},
author = {Balle, Borja and Gomrokchi, Maziar and Precup, Doina},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2130--2138},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/balle16.pdf},
url = {https://proceedings.mlr.press/v48/balle16.html},
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.}
}