@inproceedings{7cee86904d014d48aedd6232041d67bc,
title = "Private PAC learning implies finite littlestone dimension",
abstract = "We show that every approximately differentially private learning algorithm (possibly improper) for a class H with Littlestone dimension d requires Ωlog∗(d) examples. As a corollary it follows that the class of thresholds over N can not be learned in a private manner; this resolves open questions due to [Bun et al. 2015] and [Feldman and Xiao, 2015]. We leave as an open question whether every class with a finite Littlestone dimension can be learned by an approximately differentially private algorithm.",
keywords = "Differential Privacy, Littlestone dimension, PAC learning",
author = "Noga Alon and Roi Livni and Maryanthe Malliaris and Shay Moran",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019 ; Conference date: 23-06-2019 Through 26-06-2019",
year = "2019",
month = jun,
day = "23",
doi = "https://doi.org/10.1145/3313276.3316312",
language = "الإنجليزيّة",
series = "Proceedings of the Annual ACM Symposium on Theory of Computing",
publisher = "Association for Computing Machinery",
pages = "852--860",
editor = "Moses Charikar and Edith Cohen",
booktitle = "STOC 2019 - Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing",
address = "الولايات المتّحدة",
}