Data synthesis has been advocated as an important approach for utilizing data
while protecting data privacy. A large number of tabular data synthesis
algorithms (which we call synthesizers) have been proposed. Some synthesizers
satisfy Differential Privacy, while others aim to provide privacy in a
heuristic fashion. A comprehensive understanding of the strengths and
weaknesses of these synthesizers remains elusive due to lacking principled
evaluation metrics and missing head-to-head comparisons of newly developed
synthesizers that take advantage of diffusion models and large language models
with state-of-the-art marginal-based synthesizers.
In this paper, we present a principled and systematic evaluation framework
for assessing tabular data synthesis algorithms. Specifically, we examine and
critique existing evaluation metrics, and introduce a set of new metrics in
terms of fidelity, privacy, and utility to address their limitations. Based on
the proposed metrics, we also devise a unified objective for tuning, which can
consistently improve the quality of synthetic data for all methods. We
conducted extensive evaluations of 8 different types of synthesizers on 12
datasets and identified some interesting findings, which offer new directions
for privacy-preserving data synthesis.

利用扩散模型和大型语言模型的新型综合器进行对比评估，提出了一套评估指标，旨在解决现有评估指标的局限性，并为调优提供了普遍目标，以保护数据隐私和提高合成数据的质量。

朝向基于原则的表格数据合成算法评估

Towards Principled Assessment of Tabular Data Synthesis Algorithms

As the prevalence of data analysis grows, safeguarding data privacy has
become a paramount concern. Consequently, there has been an upsurge in the
development of mechanisms aimed at privacy-preserving data analyses. However,
these approaches are task-specific; designing algorithms for new tasks is a
cumbersome process. As an alternative, one can create synthetic data that is
(ideally) devoid of private information. This paper focuses on
privacy-preserving data synthesis (PPDS) by providing a comprehensive overview,
analysis, and discussion of the field. Specifically, we put forth a master
recipe that unifies two prominent strands of research in PPDS: statistical
methods and deep learning (DL)-based methods. Under the master recipe, we
further dissect the statistical methods into choices of modeling and
representation, and investigate the DL-based methods by different generative
modeling principles. To consolidate our findings, we provide comprehensive
reference tables, distill key takeaways, and identify open problems in the
existing literature. In doing so, we aim to answer the following questions:
What are the design principles behind different PPDS methods? How can we
categorize these methods, and what are the advantages and disadvantages
associated with each category? Can we provide guidelines for method selection
in different real-world scenarios? We proceed to benchmark several prominent
DL-based methods on the task of private image synthesis and conclude that
DP-MERF is an all-purpose approach. Finally, upon systematizing the work over
the past decade, we identify future directions and call for actions from
researchers.

本研究旨在提供一个全面的流派统一的方案折衷了 PPDS 领域中的两种主要方法：统计方法和基于深度学习的方法，在提供了完整的参考表格和关键点摘要以及确定了实际应用中指导方法选择的准则之后，为 DLM 方法在私密图像合成方面提供了基准测试，并鉴别出 DP-MERF 是一种全能方法。

SoK：隐私保护数据合成

SoK: Privacy-Preserving Data Synthesis

How can we release a massive volume of sensitive data while mitigating
privacy risks? Privacy-preserving data synthesis enables the data holder to
outsource analytical tasks to an untrusted third party. The state-of-the-art
approach for this problem is to build a generative model under differential
privacy, which offers a rigorous privacy guarantee. However, the existing
method cannot adequately handle high dimensional data. In particular, when the
input dataset contains a large number of features, the existing techniques
require injecting a prohibitive amount of noise to satisfy differential
privacy, which results in the outsourced data analysis meaningless. To address
the above issue, this paper proposes privacy-preserving phased generative model
(P3GM), which is a differentially private generative model for releasing such
sensitive data. P3GM employs the two-phase learning process to make it robust
against the noise, and to increase learning efficiency (e.g., easy to
converge). We give theoretical analyses about the learning complexity and
privacy loss in P3GM. We further experimentally evaluate our proposed method
and demonstrate that P3GM significantly outperforms existing solutions.
Compared with the state-of-the-art methods, our generated samples look fewer
noises and closer to the original data in terms of data diversity. Besides, in
several data mining tasks with synthesized data, our model outperforms the
competitors in terms of accuracy.

提出了一种基于隐私保护的分阶段生成模型（P3GM），通过采用分阶段的学习过程，P3GM 可以更好地处理高维数据的隐私保护数据合成问题，并在数据挖掘中的合成数据任务中表现出更高的准确性。