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. The backbone
technology of tabular synthesizers is rooted in image generative models,
ranging from Generative Adversarial Networks (GANs) to recent diffusion models.
Recent prior work sheds light on the utility-privacy tradeoff on tabular data,
revealing and quantifying privacy risks on synthetic data. We first conduct an
exhaustive empirical analysis, highlighting the utility-privacy tradeoff of
five state-of-the-art tabular synthesizers, against eight privacy attacks, with
a special focus on membership inference attacks. Motivated by the observation
of high data quality but also high privacy risk in tabular diffusion, we
propose DP-TLDM, 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
DP-TLDM 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, DP-TLDM 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.

合成数据和生成模型在隐私保护的数据共享解决方案中迅速崛起，并通过在表格综合机上实施全面的实证分析，突出了五种最先进表格综合机的实用 - 隐私权衡，提出了一个新的差分隐私表格潜在扩散模型，称为 DP-TLDM，能够在保持可比较的隐私风险水平的同时，显著提高合成数据的实用性。

衡量和减轻表格生成模型的隐私风险

Quantifying and Mitigating Privacy Risks for Tabular Generative Models

Synthetic health data have the potential to mitigate privacy concerns when
sharing data to support biomedical research and the development of innovative
healthcare applications. Modern approaches for data generation based on machine
learning, generative adversarial networks (GAN) methods in particular, continue
to evolve and demonstrate remarkable potential. Yet there is a lack of a
systematic assessment framework to benchmark methods as they emerge and
determine which methods are most appropriate for which use cases. In this work,
we introduce a generalizable benchmarking framework to appraise key
characteristics of synthetic health data with respect to utility and privacy
metrics. We apply the framework to evaluate synthetic data generation methods
for electronic health records (EHRs) data from two large academic medical
centers with respect to several use cases. The results illustrate that there is
a utility-privacy tradeoff for sharing synthetic EHR data. The results further
indicate that no method is unequivocally the best on all criteria in each use
case, which makes it evident why synthetic data generation methods need to be
assessed in context.

本文介绍了一种通用的基准测试框架，以评估生成对抗网络等方法合成的医疗数据的实用性和隐私度量，并发现在共享用于电子健康记录的合成数据时存在实用性和隐私度量的折中。