  In this paper, we revisit the problem of private stochastic convex
optimization. We propose an algorithm based on noisy mirror descent, which
achieves optimal rates both in terms of statistical complexity and number of
queries to a first-order stochastic oracle in the regime when the privacy
parameter is inversely proportional to the number of samples.
