Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. (arXiv:2303.18047v1 [cs.LG])

In this paper, we revisit the problem of Differentially Private Stochastic
Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces.
Specifically, we focus on three settings that are still far from well
understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean
space; (2) unconstrained DP-SCO in $\ell_p^d$ space; (3) DP-SCO with
heavy-tailed data over a constrained and bounded set in $\ell_p^d$ space. For
problem (1), for both convex and strongly convex loss functions, we propose
methods whose …

data excess focus functions general loss non optimization private problem risks settings space