2017
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
NeurIPS 2017poster
In this paper we study differentially private Empirical Risk Minimization(ERM) in different settings. For smooth (strongly) convex loss function with or without (non)-smooth regularization, we give algorithms which achieve either optimal or near optimal utility bound with less gradient complexity co…