Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems

Tomás González, Cristóbal Guzmán, Courtney Paquette

We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the ${\ell }_1$ setting. We propose $(\epsilon ,\delta )$-DP algorithms based on stochast…