Tight lower bounds for Differentially Private ERM

Daogao Liu, Zhou Lu

Keywords: Differential Privacy, Empirical Risk Minimization, Lower bounds

Abstract:

We consider the lower bounds of differentially private ERM for general convex functions. For approximate-DP, the well-known upper bound of DP-ERM is $O(\frac{\sqrt{p\log(1/\delta)}}{\epsilon n})$, which is believed to be tight. However, current lower bounds are off by some logarithmic terms, in particular $\Omega(\frac{\sqrt{p}}{\epsilon n})$ for constrained case and $\Omega(\frac{\sqrt{p}}{\epsilon n \log p})$ for unconstrained case.

We achieve tight $\Omega(\frac{\sqrt{p \log(1/\delta)}}{\epsilon n})$ lower bounds for both cases by introducing a novel biased mean property for fingerprinting codes. As for pure-DP, we utilize a novel $\ell_2$ loss function instead of linear functions considered by previous papers, and achieve the first (tight) $\Omega(\frac{p}{\epsilon n})$ lower bound. We also introduce an auxiliary dimension to simplify the computation brought by $\ell_2$ loss. Our results close a gap in our understanding of DP-ERM by presenting the fundamental limits. Our techniques may be of independent interest, which help enrich the tools so that it readily applies to problems that are not (easily) reducible from one-way marginals.

One-sentence Summary: We construct the first tight lower bounds for differentially private empirical risk minimization (ERM).