DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning

In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data augmentation techniques specifically designed for the constraints of differentially private learning.