Optimal Rates for Pure {\varepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails
arXiv:2604.06492v1 Announce Type: cross
Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on the worst-case Lipschitz parameter of the loss, we…