Differentially Private Online-to-Batch for Smooth Losses
Machine Learning
2022-10-14 v1 Cryptography and Security
Abstract
We develop a new reduction that converts any online convex optimization algorithm suffering regret into an -differentially private stochastic convex optimization algorithm with the optimal convergence rate on smooth losses in linear time, forming a direct analogy to the classical non-private "online-to-batch" conversion. By applying our techniques to more advanced adaptive online algorithms, we produce adaptive differentially private counterparts whose convergence rates depend on apriori unknown variances or parameter norms.
Cite
@article{arxiv.2210.06593,
title = {Differentially Private Online-to-Batch for Smooth Losses},
author = {Qinzi Zhang and Hoang Tran and Ashok Cutkosky},
journal= {arXiv preprint arXiv:2210.06593},
year = {2022}
}