ICML 2019oral8 citations
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Abstract
We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve $(\alpha,\beta)$-PAC learning and $(\epsilon,\delta)$-differential privacy using a sample of size $\tilde{O}\left(\frac{1}{\alpha\epsilon}k\log d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in the union of polygons.
BibTeX
@InProceedings{pmlr-v97-kaplan19a,
title = {Differentially Private Learning of Geometric Concepts},
author = {Kaplan, Haim and Mansour, Yishay and Matias, Yossi and Stemmer, Uri},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {3233--3241},
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/kaplan19a/kaplan19a.pdf},
url = {https://proceedings.mlr.press/v97/kaplan19a.html},
abstract = {We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve $(\alpha,\beta)$-PAC learning and $(\epsilon,\delta)$-differential privacy using a sample of size $\tilde{O}\left(\frac{1}{\alpha\epsilon}k\log d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in the union of polygons.}
}