Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problems.

In this work, we conduct the first systematic study of stochastic variational inequality (SVI) and stochastic saddle point (SSP) problems under the constraint of differential privacy-(DP). We propose two algorithms: Noisy Stochastic Extragradient (NSEG) and Noisy Inexact Stochastic Proximal Point (NISPP). We show that sampling with replacement variants of these algorithms attain the optimal risk for DP-SVI and DP-SSP. Key to our analysis is the investigation of algorithmic stability bounds, both of which are new even in the nonprivate case, together with a novel "stability implies generalization" result for the gap functions for SVI and SSP problems. The dependence of the running time of these algorithms, with respect to the dataset size $n$, is $n^2$ for NSEG and $\widetilde{O}(n^{3/2})$ for NISPP.