Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
Eli Chien Pan Li
We study the Differential Privacy (DP) guarantee of hidden-state Noisy-SGD algorithms over a bounded domain. Standard privacy analysis for Noisy-SGD assumes all internal states are revealed, which leads to a divergent R\'enyi DP bound with respect to the number of iterations. Ye & Shokri (2022) and Altschuler & Talwar (2022) proved convergent bounds for smooth (strongly) convex losses, and raise open questions about whether these assumptions can be relaxed. We provide positive answers by proving convergent R\'enyi DP bound for non-convex non-smooth losses, where we show that requiring losses to have H\"older continuous gradient is sufficient. We also provide a strictly better privacy bound compared to state-of-the-art results for smooth strongly convex losses. Our analysis relies on the improvement of shifted divergence analysis in multiple aspects, including forward Wasserstein distance tracking, identifying the optimal shifts allocation, and the  H\"older reduction lemma. Our results further elucidate the benefit of hidden-state analysis for DP and its applicability.
privacy, differential privacy, hidden state SGD, nonconvex, nonsmooth
We show the convergent privacy loss of hidden state noisy SGD even without convexity and smoothness assumptions.