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 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.

数据隐私保护是研究者们越来越关注的问题之一。扩散模型（DMs），尤其是严格的差分隐私，可以潜在地生成既具有高度隐私性又具有视觉质量的图像。然而，在保护特定数据属性的隐私方面存在挑战，目前的模型在这方面通常表现不佳。为了解决这些挑战，我们引入了PAC隐私保护扩散模型，该模型利用扩散原理并确保可能近似正确（PAC）隐私。我们通过将一个私有分类器引导集成到Langevin采样过程中来增强隐私保护。此外，鉴于测量模型隐私存在的差距，我们开发了一种新的度量标准来衡量隐私水平。我们的模型通过这个新的度量标准进行评估，并通过PAC上界的高斯矩阵计算，在基准测试中显示出优于现有领先的私有生成模型的隐私保护性能。