Differentially Private Learning of Geometric Concepts

Haim Kaplan; Yishay Mansour; Yossi Matias; Uri Stemmer

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arXiv · 1902.05017

Differentially Private Learning of Geometric Concepts

Haim Kaplan·Yishay Mansour·Yossi Matias·Uri Stemmer

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Abstract

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve $(α,β)$-PAC learning and $(ε,δ)$-differential privacy using a sample of size $\tilde{O}\left(\frac{1}{αε}k\log d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in the union of polygons.

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Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer. 2019-02-13. Differentially Private Learning of Geometric Concepts. https://arxiv.org/abs/1902.05017

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