论文标题
将合成数据集的数量集成到“先验”合成方法中
On integrating the number of synthetic data sets $m$ into the 'a priori' synthesis approach
论文作者
论文摘要
直到最近，始终将多个合成数据集释放给分析师，以允许获得有效的推论。但是，在某些条件下 - 包括使用饱和计数模型合成分类数据时 - 单次插补（$ m = 1 $）就足够了。然而，增加$ m $会导致实用性改善，但以较高的风险为代价，这是风险实用权衡的一个例子。因此，问题是：相对于风险实现权衡，$ m $的哪个值是最佳的？此外，本文考虑了分析分类数据集的两种方法：由于它们具有偶性表表示，因此可以在分析之前平均多个分类数据集，而不是平均分析后的通常方式。本文还介绍了一对指标，$τ_3（k，d）$和$τ_4（k，d）$，这些指标适用于评估多个分类合成数据集中的披露风险。最后，综合方法得到了经验证明。
Until recently, multiple synthetic data sets were always released to analysts, to allow valid inferences to be obtained. However, under certain conditions - including when saturated count models are used to synthesize categorical data - single imputation ($m=1$) is sufficient. Nevertheless, increasing $m$ causes utility to improve, but at the expense of higher risk, an example of the risk-utility trade-off. The question, therefore, is: which value of $m$ is optimal with respect to the risk-utility trade-off? Moreover, the paper considers two ways of analysing categorical data sets: as they have a contingency table representation, multiple categorical data sets can be averaged before being analysed, as opposed to the usual way of averaging post-analysis. This paper also introduces a pair of metrics, $τ_3(k,d)$ and $τ_4(k,d)$, that are suited for assessing disclosure risk in multiple categorical synthetic data sets. Finally, the synthesis methods are demonstrated empirically.
