用截断树结构生成数据，兼顾隐私保护与实用性能。
TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility
- 基于截断的藤蔓耦合模型，动态控制数据依赖关系。
- 在真实和模拟数据上均实现更优的隐私-效用平衡。
- 适合需要高隐私保障的医疗、金融等敏感数据场景。
我们提出TVineSynth，一种基于藤蔓耦合的合成表格数据生成器，旨在平衡隐私与效用。与通过全局加噪实现差分隐私（DP）的方法不同，TVineSynth通过对数据生成分布进行可控近似，避免了下游预测任务中合成数据效用下降的问题。该方法引入针对性偏差，在藤蔓树结构的约束下，使模型自动消除泄露隐私的依赖关系，同时保留对预测有用的关联。隐私性通过成员身份攻击（MIA）和属性推断攻击（AIA）评估。理论上证明了其在连续敏感属性下的抗属性推断能力。在模拟与真实数据集上的对比实验表明，无论是否启用差分隐私，TVineSynth均显著优于现有方法，在隐私与效用间达到更优平衡。
原文摘要 · Abstract (English)
We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve DP by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution, so that it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model that, combined with the specific tree structure of the vine, causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. Privacy is here measured with membership (MIA) and attribute inference attacks (AIA). Further, we theoretically justify how the construction of TVineSynth ensures AIA privacy under a natural privacy measure for continuous sensitive attributes. When compared to competitor models, with and without DP, on simulated and on real-world data, TVineSynth achieves a superior privacy-utility balance.
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