2026
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
AAAI 2026technical
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 r