  Data augmentation techniques, such as simple image transformations and
combinations, are highly effective at improving the generalization of computer
vision models, especially when training data is limited. However, such
techniques are fundamentally incompatible with differentially private learning
approaches, due to the latter's built-in assumption that each training image's
contribution to the learned model is bounded. 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. Our first technique, DP-Mix_Self, achieves SoTA classification
performance across a range of datasets and settings by performing mixup on
self-augmented data. Our second technique, DP-Mix_Diff, further improves
performance by incorporating synthetic data from a pre-trained diffusion model
into the mixup process. We open-source the code at
https://github.com/wenxuan-Bao/DP-Mix.
