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.

本文提出一种学习差分隐私概率模型（DPPM），用于生成具有差分隐私保证的高分辨率图像，通过训练模型以适合训练数据的分布并在过程中执行随机响应机制，然后进行哈密顿动力学采样以及由训练概率模型预测的差分隐私运动方向，以获得保护隐私信息的图像，相比其他最先进的差分隐私生成方法，我们的方法可以生成高达256x256具有显着视觉质量和数据实用性的图像，大量实验表明我们的方法的有效性。

学习差分隐私概率模型保护隐私的图像生成