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Over the past three decades, synthetic data methods for statisticaldisclosure control have continually evolved, but mainly within the domain ofsurvey data sets. There are certain characteristics of administrativedatabases, such as their size, which present challenges from a synthesisperspective and require special attention. This paper, through the fitting ofsaturated count models, presents a synthesis method that is suitable foradministrative databases that is tuned by two parameters. The method allowslarge categorical data sets to be synthesized quickly and allows risk andutility metrics to be satisfied a priori, that is, prior to synthetic datageneration. The paper explores how the flexibility afforded by two-parametercount models (the negative binomial and Poisson-inverse Gaussian) can beutilised to protect respondents' - especially uniques' - privacy in syntheticdata. Finally, an empirical example is carried out through the synthesis of adatabase which can be viewed as a good substitute to the English School Census.

Using saturated count models for user-friendly synthesis of categorical data