On Differentially Private Stochastic Convex Optimization with
Heavy-tailed Data
On Differentially Private Stochastic Convex Optimization with
Heavy-tailed Data
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, resulting in failure to provide the DP guarantees. To better understand …