Using Saturated Count Models for User-Friendly Synthesis of Large Confidential Administrative Databases
本文提出一种适用于大型行政数据库的合成方法，通过拟合饱和计数模型并调节两个参数，快速合成大型分类数据集，并能在生成前满足风险和效用指标，保护受访者隐私。
Abstract Over the past three decades, synthetic data methods for statistical disclosure control have continually evolved, but mainly within the domain of survey data sets. There are certain characteristics of administrative databases, such as their size, which present challenges from a synthesis perspective and require special attention. This paper, through the fitting of saturated count models, presents a synthesis method that is suitable for administrative databases. It is tuned by two parameters, σ and α. The method allows large categorical data sets to be synthesized quickly and allows risk and utility metrics to be satisfied a priori, that is, prior to synthetic data generation. The paper explores how the flexibility afforded by two-parameter count models (the negative binomial and Poisson-inverse Gaussian) can be utilised to protect respondents'—especially uniques'—privacy in synthetic data. Finally, an empirical example is carried out through the synthesis of a database which can be viewed as a good substitute to the English School Census.