Learning Differentially Private Probabilistic Models for
  Privacy-Preserving Image Generation
Bochao Liu Shiming Ge Pengju Wang Liansheng Zhuang Tongliang Liu
A number of deep models trained on high-quality and valuable images have been
deployed in practical applications, which may pose a leakage risk of data
privacy. Learning differentially private generative models can sidestep this
challenge through indirect data access. However, such differentially private
generative models learned by existing approaches can only generate images with
a low-resolution of less than 128x128, hindering the widespread usage of
generated images in downstream training. In this work, we propose learning
differentially private probabilistic models (DPPM) to generate high-resolution
images with differential privacy guarantee. In particular, we first train a
model to fit the distribution of the training data and make it satisfy
differential privacy by performing a randomized response mechanism during
training process. Then we perform Hamiltonian dynamics sampling along with the
differentially private movement direction predicted by the trained
probabilistic model to obtain the privacy-preserving images. In this way, it is
possible to apply these images to different downstream tasks while protecting
private information. Notably, compared to other state-of-the-art differentially
private generative approaches, our approach can generate images up to 256x256
with remarkable visual quality and data utility. Extensive experiments show the
effectiveness of our approach.