We study differentially private stochastic convex optimization (DP-SCO) under user-level privacy where each user may hold multiple data items. Existing work for user-level DP-SCO either requires super-polynomial runtime or requires number of users that grows polynomially with the dimensionality of the problem. We develop new algorithms for user-level DP-SCO that obtain optimal rates, run in polynomial time, and require a number of users that grow logarithmically in the dimension. Moreover, our algorithms are the first to obtain optimal rates for non-smooth functions in polynomial time. These…Apple Machine Learning Research