标题： DP-TLDM：差分隐私表格潜在扩散模型 显示英文标题

标题： DP-TLDM: Differentially Private Tabular Latent Diffusion Model

摘要： 生成模型的合成数据作为一种保护隐私的数据共享解决方案浮现出来。 这样的合成数据集应在不泄露可识别的私人信息的情况下，与原始数据相似。 到目前为止，先前的研究主要集中在有限类型的表格合成器和少量的隐私攻击上，特别是对生成对抗网络的攻击，并忽略了成员推断攻击和防御策略，即差分隐私。 受保持合成数据表的高数据质量和低隐私风险这一困境的驱动，我们提出了DPTLDM，即差分隐私表格潜在扩散模型，该模型由一个自动编码器网络来编码表格数据，以及一个潜在扩散模型来合成潜在表格。 在新兴的f-DP框架下，我们应用DP-SGD来训练自动编码器，结合批次裁剪，并使用分离值作为隐私度量，以更好地捕捉差分隐私算法带来的隐私收益。 我们的实证评估表明，DPTLDM能够在实现有意义的理论隐私保证的同时，显著提高合成数据的效用。 具体而言，与其他受差分隐私保护的表格生成模型相比，DPTLDM在数据相似性方面平均提高了35%，在下游任务的效用方面提高了15%，在数据可区分性方面提高了50%，同时保持了相当水平的隐私风险。 显示英文摘要

摘要： Synthetic data from generative models emerges as the privacy-preserving data sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. Till date, the prior focus on limited types of tabular synthesizers and a small number of privacy attacks, particularly on Generative Adversarial Networks, and overlooks membership inference attacks and defense strategies, i.e., differential privacy. Motivated by the conundrum of keeping high data quality and low privacy risk of synthetic data tables, we propose DPTLDM, Differentially Private Tabular Latent Diffusion Model, which is composed of an autoencoder network to encode the tabular data and a latent diffusion model to synthesize the latent tables. Following the emerging f-DP framework, we apply DP-SGD to train the auto-encoder in combination with batch clipping and use the separation value as the privacy metric to better capture the privacy gain from DP algorithms. Our empirical evaluation demonstrates that DPTLDM is capable of achieving a meaningful theoretical privacy guarantee while also significantly enhancing the utility of synthetic data. Specifically, compared to other DP-protected tabular generative models, DPTLDM improves the synthetic quality by an average of 35% in data resemblance, 15% in the utility for downstream tasks, and 50% in data discriminability, all while preserving a comparable level of privacy risk.