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Private PAC learning implies finite Littlestone dimension
lib:dafa92434d8ab837 (v1.0.0)
Authors: Noga Alon,Roi Livni,Maryanthe Malliaris,Shay Moran
ArXiv: 1806.00949
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1806.00949v3
We show that every approximately differentially private learning algorithm (possibly improper) for a class $H$ with Littlestone dimension~$d$ requires $\Omega\bigl(\log^*(d)\bigr)$ examples. As a corollary it follows that the class of thresholds over $\mathbb{N}$ can not be learned in a private manner; this resolves open question due to [Bun et al., 2015, 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.
ArXiv: 1806.00949
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1806.00949v3
We show that every approximately differentially private learning algorithm (possibly improper) for a class $H$ with Littlestone dimension~$d$ requires $\Omega\bigl(\log^*(d)\bigr)$ examples. As a corollary it follows that the class of thresholds over $\mathbb{N}$ can not be learned in a private manner; this resolves open question due to [Bun et al., 2015, 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.
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