Abstract
Differentially private (DP) image synthesis aims to generate synthetic images from a sensitive dataset, alleviating the privacy leakage concerns of organizations sharing and utilizing synthetic images. Although previous methods have significantly progressed, especially in training diffusion models on sensitive images with DP Stochastic Gradient Descent (DP-SGD), they still suffer from unsatisfactory performance. In this work, inspired by curriculum learning, we propose a two-stage DP image synthesis framework, where diffusion models learn to generate DP synthetic images from easy to hard. Unlike existing methods that directly use DP-SGD to train diffusion models, we propose an easy stage in the beginning, where diffusion models learn simple features of the sensitive images. To facilitate this easy stage, we propose to use `central images', simply aggregations of random samples of the sensitive dataset. Intuitively, although those central images do not show details, they demonstrate useful characteristics of all images and only incur minimal privacy costs, thus helping early-phase model training. We conduct experiments to present that on the average of four investigated image datasets, the fidelity and utility metrics of our synthetic images are 33.1% and 2.1% better than the state-of-the-art method.
Abstract (translated)
差分隐私（DP）图像合成的目标是从敏感数据集生成合成图像，以减轻组织在共享和利用这些合成图像时的隐私泄露担忧。尽管先前的方法已取得了显著进步，尤其是在使用差分隐私随机梯度下降法（DP-SGD）对敏感图像训练扩散模型方面，它们仍然存在性能不理想的状况。在这项工作中，受课程学习的启发，我们提出了一种两阶段的DP图像合成框架，在此框架中，扩散模型从易到难逐步学习生成DP合成图像。不同于现有的直接使用DP-SGD来训练扩散模型的方法，我们在初始阶段提出了一个“简单”阶段，使扩散模型能够学习敏感图像的基本特征。为了促进这一简单的学习阶段，我们提议采用“中心图像”，即敏感数据集中随机样本的简单聚合物。直观地讲，尽管这些中心图并没有展示细节信息，但它们展示了所有图像的重要特性，并且只带来极小的隐私成本，从而有助于早期模型训练。我们的实验表明，在四个研究的图像数据集上的平均表现中，我们生成的合成图像在保真度和实用性指标上分别比现有最佳方法高出33.1%和2.1%。
URL
https://arxiv.org/abs/2504.01395
