Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables

Bon Joshua J. (), Baffour Bernard (), Spallek Melanie () and Haynes Michele ()
Additional contact information
Bon Joshua J.: Queensland University of Technology, School of Mathematical Sciences, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
Baffour Bernard: Australian National University, School of Demography, 9 Fellows Road, Acton, ACT 2601, Australia.
Spallek Melanie: Australian Catholic University, Institute for Learning Sciences and Teacher Education, 229 Elizabeth St, Brisbane, Queensland, 4000, Australia.
Haynes Michele: Australian Catholic University, Institute for Learning Sciences and Teacher Education, 229 Elizabeth St, Brisbane, Queensland, 4000, Australia.

Journal of Official Statistics, 2020, vol. 36, issue 2, 275-296

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.

Keywords: Count data; log-linear model; marginal model; privacy restriction (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2020-0015 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:36:y:2020:i:2:p:275-296:n:4

DOI: 10.2478/jos-2020-0015

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().