RAPID：属性预测诱导披露的风险
RAPID: Risk of Attribute Prediction-Induced Disclosure in Synthetic Microdata
AI总结：
RAPID是一种用于评估属性推断披露风险的度量方法，通过模拟和真实数据验证其在合成数据生成中的有效性。
摘要：
统计数据匿名化越来越多地依赖于完全合成的微观数据，其中经典的身份披露度量在对抗者能够从发布的数据中推断出敏感属性的能力方面显得信息较少。我们介绍了RAPID（属性预测诱导披露的风险），这是一种披露风险度量，直接量化了在现实攻击模型下的推断脆弱性。攻击者仅在发布的合成数据上训练预测模型，并将其应用于真实个体的准标识符。对于连续敏感属性，RAPID报告预测值落在指定相对误差容限内的记录比例。对于分类属性，我们提出了一种基线归一化的置信度分数，该分数衡量攻击者对真实类别比仅凭类别流行度预期的置信度更高多少，并将风险总结为超过政策定义阈值的记录比例。这种构造产生了一个可解释、有界的风险度量，具有对类别不平衡的鲁棒性，不依赖于任何特定的合成器，并且可以应用于任意学习算法。我们通过模拟和真实数据展示了阈值校准、不确定性量化和合成数据生成器的比较评估。我们的结果表明，RAPID提供了一个实用的、针对攻击者的属性推断披露风险上界，补充了现有的效用诊断和披露控制框架。
英文摘要：
Statistical data anonymization increasingly relies on fully synthetic microdata, for which classical identity disclosure measures are less informative than an adversary's ability to infer sensitive attributes from released data. We introduce RAPID (Risk of Attribute Prediction--Induced Disclosure), a disclosure risk measure that directly quantifies inferential vulnerability under a realistic attack model. An adversary trains a predictive model solely on the released synthetic data and applies it to real individuals' quasi-identifiers. For continuous sensitive attributes, RAPID reports the proportion of records whose predicted values fall within a specified relative error tolerance. For categorical attributes, we propose a baseline-normalized confidence score that measures how much more confident the attacker is about the true class than would be expected from class prevalence alone, and we summarize risk as the fraction of records exceeding a policy-defined threshold. This construction yields an interpretable, bounded risk metric that is robust to class imbalance, independent of any specific synthesizer, and applicable with arbitrary learning algorithms. We illustrate threshold calibration, uncertainty quantification, and comparative evaluation of synthetic data generators using simulations and real data. Our results show that RAPID provides a practical, attacker-realistic upper bound on attribute-inference disclosure risk that complements existing utility diagnostics and disclosure control frameworks.
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