@article{e125fc17d380425bbb015c246127e43c,
title = "DIFFERENTIALLY PRIVATE LEARNING OF GEOMETRIC CONCEPTS",
abstract = "We present efficient differentially private algorithms for learning unions of polygons in the plane (which are not necessarily convex). Our algorithms are (α , β)-probably approximately correct and (ϵ , δ )-differentially private using a sample of size O ( 1 α) , where the domain is [d] × [d] and k is the number of edges in the union of polygons. Our algorithms are obtained by designing a private variant of the classical (nonprivate) learner for conjunctions using the greedy algorithm for set cover.",
keywords = "PAC learning, differential privacy, polygons",
author = "Haim Kaplan and Yishay Mansour and Yossi Matias and Uri Stemmer",
note = "Publisher Copyright: {\textcopyright} 2022 Society for Industrial and Applied Mathematics.",
year = "2022",
doi = "10.1137/21M1427450",
language = "אנגלית",
volume = "32",
pages = "952--974",
journal = "SIAM Journal on Optimization",
issn = "1052-6234",
publisher = "Society for Industrial and Applied Mathematics (SIAM)",
number = "3",
}