ICML 2019oral192 citations
Graphical-model based estimation and inference for differential privacy
Ryan Mckenna, Daniel Sheldon, Gerome Miklau
Abstract
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.
BibTeX
@InProceedings{pmlr-v97-mckenna19a,
title = {Graphical-model based estimation and inference for differential privacy},
author = {Mckenna, Ryan and Sheldon, Daniel and Miklau, Gerome},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4435--4444},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/mckenna19a/mckenna19a.pdf},
url = {https://proceedings.mlr.press/v97/mckenna19a.html},
abstract = {Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.}
}