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Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables

Author

Listed:
  • 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.)

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.

Suggested Citation

  • Bon Joshua J. & Baffour Bernard & Spallek Melanie & Haynes Michele, 2020. "Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables," Journal of Official Statistics, Sciendo, vol. 36(2), pages 275-296, June.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:2:p:275-296:n:4
    DOI: 10.2478/jos-2020-0015
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    References listed on IDEAS

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    1. Jarod Y. L. Lee & James J. Brown & Louise M. Ryan, 2017. "Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 202-208, July.
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