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

Jackson, James, Mitra, Robin ORCID: https://orcid.org/0000-0001-9584-8044, 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 185 (4) , pp. 1613-1643. 10.1111/rssa.12876

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Royal Statistical Society
ISSN:	0964-1998
Date of First Compliant Deposit:	18 August 2022
Date of Acceptance:	20 April 2022
Last Modified:	07 May 2023 17:40
URI:	https://orca.cardiff.ac.uk/id/eprint/152012

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