DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
Publication Date: 12/10/2023
Event: NeurIPS 2023
Reference: pp. 1-17, 2023
Authors: Wenxuan Bao, University of Florida; Francesco Pittaluga, NEC Laboratories America, Inc.; Vijay BG Kumar, NEC Laboratories America, Inc.; Vincent Bindshaedler, University of Florida
Abstract: Data augmentation techniques, such as 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 latters built-in assumption that each training images 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.
Publication Link: https://proceedings.neurips.cc/paper_files/paper/2023/file/28484cee66f27fa070796b631cc5242d-Paper-Conference.pdf
