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Using saturated count models for user-friendly synthesis of large confidential administrative databases

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>16/08/2022
<mark>Journal</mark>Journal of the Royal Statistical Society: Series A Statistics in Society
Number of pages31
Publication StatusE-pub ahead of print
Early online date16/08/22
<mark>Original language</mark>English

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 syn-thesis 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,𝜎and𝛼.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.