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.

翻译：大量基于高质量和有价值图像训练的深度模型已部署于实际应用，但这可能引发数据隐私泄露风险。学习差分隐私生成模型可通过间接数据访问规避这一挑战。然而，现有方法所学习的差分隐私生成模型仅能生成分辨率低于128×128的图像，阻碍了生成图像在下游训练中的广泛使用。本文提出学习差分隐私概率模型（DPPM），在保证差分隐私的前提下生成高分辨率图像。具体而言，我们首先训练一个模型拟合训练数据分布，并通过在训练过程中执行随机响应机制使其满足差分隐私。随后，我们结合训练概率模型预测的差分隐私移动方向，执行哈密顿动力学采样，以获得隐私保护图像。通过这种方式，这些图像可在保护隐私信息的同时应用于不同下游任务。值得注意的是，与其他最先进的差分隐私生成方法相比，我们的方法能够生成分辨率高达256×256的图像，并具有显著的视觉质量和数据效用。大量实验证明了我们方法的有效性。