Discrete Distribution Estimation Under Local Privacy
Abstract
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy. This paper examines discrete distribution estimation under local privacy, a setting wherein service providers can learn the distribution of a categorical statistic of interest without collecting the underlying data. We present new mechanisms, including hashed k-ary Randomized Response (KRR), that empirically meet or exceed the utility of existing mechanisms at all privacy levels. New theoretical results demonstrate the order-optimality of KRR and the existing RAPPOR mechanism at different privacy regimes.
Cite
Text
Kairouz et al. "Discrete Distribution Estimation Under Local Privacy." International Conference on Machine Learning, 2016.Markdown
[Kairouz et al. "Discrete Distribution Estimation Under Local Privacy." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/kairouz2016icml-discrete/)BibTeX
@inproceedings{kairouz2016icml-discrete,
title = {{Discrete Distribution Estimation Under Local Privacy}},
author = {Kairouz, Peter and Bonawitz, Keith and Ramage, Daniel},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {2436-2444},
volume = {48},
url = {https://mlanthology.org/icml/2016/kairouz2016icml-discrete/}
}