While the success of deep learning relies on large amounts of training datasets, data is often limited in privacy-sensitive domains. To address this challenge, generative model learning with differential privacy has emerged as a solution to train private generative models for desensitized data generation. However, the quality of the images generated by existing methods is limited due to the complexity of modeling data distribution. We build on the success of diffusion models and introduce DP-SAD, which trains a private diffusion model by a stochastic adversarial distillation method. Specifically, we first train a diffusion model as a teacher and then train a student by distillation, in which we achieve differential privacy by adding noise to the gradients from other models to the student. For better generation quality, we introduce a discriminator to distinguish whether an image is from the teacher or the student, which forms the adversarial training. Extensive experiments and analysis clearly demonstrate the effectiveness of our proposed method.

本研究解决了在隐私敏感领域训练生成模型时数据受限的问题。通过引入DP-SAD方法，结合随机对抗蒸馏训练私有扩散模型，显著提高了生成图像的质量。研究表明，该方法有效提升了生成模型的隐私保护和图像质量，具有较大的应用潜力。

通过随机对抗蒸馏学习差分隐私扩散模型