基于Holdout的混合类型合成数据的保真度和隐私性评估
Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data
摘要：
基于人工智能的数据合成在过去几年中取得了快速进展，并且越来越多地被认可其承诺能够实现尊重隐私的高保真数据共享。然而，充分评估生成的合成数据集的质量仍然是一个开放的挑战。我们介绍并展示了一个基于坚持的实证评估框架，用于量化混合类型表格数据的合成数据解决方案的保真度和隐私风险。测量保真度是基于低维边缘分布的统计距离，这为合成数据集的代表性提供了一个无模型且易于沟通的经验度量。隐私风险通过计算与训练数据最接近的记录的个体水平距离来评估。通过证明合成样本与训练样本和保持数据一样接近，我们得到了强有力的证据，证明合成器确实学会了概括模式，并且独立于单个训练记录。我们展示了四个混合类型数据集的七个不同的合成数据解决方案的框架，并将其与更传统的统计披露技术进行比较。研究结果强调，需要系统地评估这些新兴的合成数据生成器的保真度以及隐私。
英文摘要：
AI-based data synthesis has seen rapid progress over the last several years, and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing. However, adequately evaluating the quality of generated synthetic datasets is still an open challenge. We introduce and demonstrate a holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions for mixed-type tabular data. Measuring fidelity is based on statistical distances of lower-dimensional marginal distributions, which provide a model-free and easy-to-communicate empirical metric for the representativeness of a synthetic dataset. Privacy risk is assessed by calculating the individual-level distances to closest record with respect to the training data. By showing that the synthetic samples are just as close to the training as to the holdout data, we yield strong evidence that the synthesizer indeed learned to generalize patterns and is independent of individual training records. We demonstrate the presented framework for seven distinct synthetic data solutions across four mixed-type datasets and compare these to more traditional statistical disclosure techniques. The results highlight the need to systematically assess the fidelity just as well as the privacy of these emerging class of synthetic data generators.
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