• Corpus ID: 235294227

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

@article{Kamath2022ImprovedRF,
title={Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data},
author={Gautam Kamath and Xingtu Liu and Huanyu Zhang},
journal={ArXiv},
year={2022},
volume={abs/2106.01336}
}
• Published 2 June 2021
• Computer Science, Mathematics
• ArXiv
We study stochastic convex optimization with heavy-tailed data under the constraint of diﬀerential privacy (DP). Most prior work on this problem is restricted to the case where the loss function is Lipschitz. Instead, as introduced by Wang, Xiao, Devadas, and Xu [WXDX20], we study general convex loss functions with the assumption that the distribution of gradients has bounded k -th moments. We provide improved upper bounds on the excess population risk under concentrated DP for convex and…

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