Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables

Journal article

Joshua J. Bon, Bernard Baffour, Melanie Spallek and Michele Haynes. (2020). Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables. Journal of Official Statistics. 36(2), pp. 275-296. https://doi.org/10.2478/jos-2020-0015
AuthorsJoshua J. Bon, Bernard Baffour, Melanie Spallek and Michele Haynes
Abstract

Contingency tables provide a convenient format to publish summary data from confidential survey and administrative records that capture a wide range of social and economic information. By their nature, contingency tables enable aggregation of potentially sensitive data, limiting disclosure of identifying information. Furthermore, censoring or perturbation can be used to desensitise low cell counts when they arise. However, access to detailed cross-classified tables for research is often restricted by data custodians when too many censored or perturbed cells are required to preserve privacy. In this article, we describe a framework for selecting and combining log-linear models when accessible data is restricted to overlapping marginal contingency tables. The approach is demonstrated through application to housing transition data from the Australian Census Longitudinal Data set provided by the Australian Bureau of Statistics.

Year2020
JournalJournal of Official Statistics
Journal citation36 (2), pp. 275-296
PublisherSciendo
ISSN0282-423X
Digital Object Identifier (DOI)https://doi.org/10.2478/jos-2020-0015
Scopus EID2-s2.0-85089077672
Open accessPublished as ‘gold’ (paid) open access
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Deposited27 Apr 2021
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