Privacy-Preserving Generative Modeling With Sliced Wasserstein Distance

Ziniu Liu, Han Yu, Kai Chen, Aiping Li

Abstract: Large models require larger datasets. While people gain from using massive amounts of data to train large models, they must be concerned about privacy issues. To address this issue, we propose a novel approach for private generative modeling using the Sliced Wasserstein Distance (SWD) metric in a Differential Private (DP) manner. We propose Normalized Clipping, a parameter-free clipping technique that generates higher-quality images. We demonstrate the advantages of Normalized Clipping over the traditional clipping method in parameter tuning and model performance through experiments. Moreover, experimental results indicate that our model outperforms previous methods in differentially private image generation tasks.