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

Jackson, James and Mitra, Robin and Francis, Brian and Dove, Iain (2022) Using saturated count models for user-friendly synthesis of large confidential administrative databases. Journal of the Royal Statistical Society: Series A Statistics in Society. ISSN 0964-1998

Full text not available from this repository.

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,and.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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series A Statistics in Society
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
174697
Deposited By:
Deposited On:
17 Aug 2022 09:15
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Aug 2022 02:15