AAAI 2026technical0 citations
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
Hao Liang, Wanrong Zhang, Xinlei He, Kaishun Wu, Hong Xing
Abstract
Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to protect sensitive data during the training of machine learning models, but its privacy guarantee often comes at a large cost of model performance due to the lack of tight theoretical bounds quantifying privacy loss. While recent efforts have achieved more accurate privacy guarantees, they still impose some assumptions prohibited from practical applications, such as convexity and complex parameter requirements, and rarely investigate in-depth the impact of privacy mechanisms on the model
BibTeX
@inproceedings{aaai2026_animprovedprivac,
title = {An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses},
author = {Hao Liang and Wanrong Zhang and Xinlei He and Kaishun Wu and Hong Xing},
booktitle = {AAAI 2026},
year = {2026}
}