用程序生成的合成数据训练私密扩散模型，提升生成质量。
Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training
- 分三阶段分析扩散模型，选定粗略和去噪阶段用合成数据替代真实数据
- 实验表明该方法可减少隐私预算消耗，生成图像更真实、噪声更少
- 适合关注隐私保护生成模型的研究者和工业应用开发者
程序生成的合成数据已被用于分类任务的差分隐私训练，以在不泄露隐私的情况下提升性能。然而，由于合成数据源于随机过程，其分布与真实数据存在可区分性，难以有效迁移，导致使用合成数据训练的模型生成不真实的随机图像，给生成模型的应用带来挑战。本文提出 DP-SynGen，将程序生成的合成数据应用于扩散模型，以解决该问题。通过分析扩散模型的三个阶段（粗略、上下文、去噪），我们识别出可在粗略和去噪阶段无需真实数据即可有效利用合成数据的阶段。理论与实证验证表明，这两个阶段可完全替换为合成数据，从而降低隐私预算消耗。实验结果表明，DP-SynGen 能有效缓解隐私噪声对生成过程的负面影响，显著提升生成数据质量。
原文摘要 · Abstract (English)
Programmatically generated synthetic data has been used in differential private training for classification to enhance performance without privacy leakage. However, as the synthetic data is generated from a random process, the distribution of real data and the synthetic data are distinguishable and difficult to transfer. Therefore, the model trained with the synthetic data generates unrealistic random images, raising challenges to adapt the synthetic data for generative models. In this work, we propose DP-SynGen, which leverages programmatically generated synthetic data in diffusion models to address this challenge. By exploiting the three stages of diffusion models(coarse, context, and cleaning) we identify stages where synthetic data can be effectively utilized. We theoretically and empirically verified that cleaning and coarse stages can be trained without private data, replacing them with synthetic data to reduce the privacy budget. The experimental results show that DP-SynGen improves the quality of generative data by mitigating the negative impact of privacy-induced noise on the generation process.
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