Large amounts of tabular data remain underutilized due to privacy, data
quality, and data sharing limitations. While training a generative model
producing synthetic data resembling the original distribution addresses some of
these issues, most applications require additional constraints from the
generated data. Existing synthetic data approaches are limited as they
typically only handle specific constraints, e.g., differential privacy (DP) or
increased fairness, and lack an accessible interface for declaring general
specifications. In this work, we introduce ProgSyn, the first programmable
synthetic tabular data generation algorithm that allows for comprehensive
customization over the generated data. To ensure high data quality while
adhering to custom specifications, ProgSyn pre-trains a generative model on the
original dataset and fine-tunes it on a differentiable loss automatically
derived from the provided specifications. These can be programmatically
declared using statistical and logical expressions, supporting a wide range of
requirements (e.g., DP or fairness, among others). We conduct an extensive
experimental evaluation of ProgSyn on a number of constraints, achieving a new
state-of-the-art on some, while remaining general. For instance, at the same
fairness level we achieve 2.3% higher downstream accuracy than the
state-of-the-art in fair synthetic data generation on the Adult dataset.
Overall, ProgSyn provides a versatile and accessible framework for generating
constrained synthetic tabular data, allowing for specifications that generalize
beyond the capabilities of prior work.

大量的表格数据由于隐私、数据质量和数据共享限制而未完全利用。本文介绍了 ProgSyn，这是第一个可编程的合成表格数据生成算法，它允许对生成的数据进行全面的自定义。通过在原始数据集上进行预训练并根据提供的规范进行差分可微损失函数的微调，ProgSyn 可以确保高质量的数据并满足自定义规范。实验评估结果表明，ProgSyn 在多个约束条件下取得了新的最先进水平，并且具有广泛的适用性。综合而言，ProgSyn 为生成受限制的合成表格数据提供了灵活、易用的框架，允许规范超越先前的工作的能力。