Private Online Learning via Lazy Algorithms

Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar

Keywords: Differential Privacy, Online Optimization

TL;DR: Improved regret bounds for Privacy Online Learning and OCO via a general Lazy-to-Private transformation

Abstract:

We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO).
We propose a new transformation that transforms lazy online learning algorithms into private algorithms. We apply our transformation for differentially private OPE and OCO using existing lazy algorithms for these problems. Our final algorithms obtain regret which significantly improves the regret in the high privacy regime $\varepsilon \ll 1$, obtaining $\sqrt{T \log d} + T^{1/3} \log(d)/\varepsilon^{2/3}$ for DP-OPE and $\sqrt{T} + T^{1/3} \sqrt{d}/\varepsilon^{2/3}$ for DP-OCO. We also complement our results with a lower bound for DP-OPE, showing that these rates are optimal for a natural family of low-switching private algorithms.

Primary Area: Privacy

Submission Number: 19036