提出可差分隐私保护的表格数据生成算法，提升高阶关联数据的合成质量。
Differentially Private Synthetic Data via APIs 4: Tabular Data
- 基于演化机制，使用专用算子迭代优化合成数据。
- 在高阶相关数据上分类准确率提升10%，速度比最优基线快28倍。
- 适合需要隐私保护且数据结构复杂的场景，如医疗金融领域。
本文研究在差分隐私（DP）保证下生成合成表格数据的问题，以支持敏感领域的数据共享。尽管已有大量研究，现有先进方法通常聚焦于降低低阶边际查询误差，忽视了高阶相关性带来的挑战。为此，我们将原本用于图像和文本合成的私有演化（Private Evolution, PE）框架扩展至表格数据，提出针对表格数据的算法Tab-PE。Tab-PE通过迭代演化过程，利用专为表格设计的算子生成数据变体，进行私有评分并选择高质量样本保留与传播。与原版PE依赖大型基础模型不同，Tab-PE采用启发式算子，显著降低计算开销，使该方法更适用于表格数据。在真实世界和模拟数据集上的广泛实验表明，当数据存在高阶相关性时，Tab-PE明显优于以往基线。相比最佳基线AIM，Tab-PE分类准确率最高提升10%，运行速度加快28倍。
原文摘要 · Abstract (English)
This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive study, state-of-the-art methods often focus on minimizing low-order marginal query errors and overlook the challenges posed by high-order correlations. To address this gap, we extend the Private Evolution (PE) framework, originally developed for DP-compliant image and text synthesis, to tabular data. We introduce Tab-PE -- an algorithm for synthetic tabular data generation under DP constraints. Tab-PE iteratively improves a candidate dataset via an evolutionary process that leverages tabular-specialized operators to produce variations, privately scores them, and selects the highest-quality samples to retain and propagate. In contrast to the original PE, which relies on large foundation models, Tab-PE employs heuristic operators with significantly lower computational costs, making PE more practical and scalable for tabular data. Through extensive experiments on real-world and simulation datasets, we demonstrate that Tab-PE substantially outperforms prior baselines on datasets exhibiting high-order correlations. Compared to the best baseline -- AIM, Tab-PE improves classification accuracy by up to 10% while running 28 times faster.
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