Data privacy protection is garnering increased attention among researchers. Diffusion models (DMs), particularly with strict differential privacy, can potentially produce images with both high privacy and visual quality. However, challenges arise such as in ensuring robust protection in privatizing specific data attributes, areas where current models often fall short. To address these challenges, we introduce the PAC Privacy Preserving Diffusion Model, a model leverages diffusion principles and ensure Probably Approximately Correct (PAC) privacy. We enhance privacy protection by integrating a private classifier guidance into the Langevin Sampling Process. Additionally, recognizing the gap in measuring the privacy of models, we have developed a novel metric to gauge privacy levels. Our model, assessed with this new metric and supported by Gaussian matrix computations for the PAC bound, has shown superior performance in privacy protection over existing leading private generative models according to benchmark tests.

翻译：数据隐私保护正日益受到研究者的关注。扩散模型（DMs），尤其是在严格的差分隐私约束下，有潜力生成兼具高隐私性和视觉质量的图像。然而，在确保特定数据属性私有化的鲁棒保护方面仍存在挑战，当前模型在这些领域往往表现不足。为解决这些问题，我们提出了PAC隐私保护扩散模型，该模型利用扩散原理并确保概率近似正确（PAC）隐私。我们通过将私有分类器引导集成到朗之万采样过程中来增强隐私保护。此外，针对现有模型隐私度量方法的不足，我们开发了一种新的指标来衡量隐私水平。我们的模型使用这一新指标进行评估，并借助高斯矩阵计算为PAC界提供支持，基准测试表明其在隐私保护方面优于现有的领先私有生成模型。