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Disclosure-protected Inference Using Generalised Linear Models

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  • James O. Chipperfield
  • Christine M. O'Keefe

Abstract

type="main" xml:id="insr12054-abs-0001"> Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro-data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real-life case studies and in simulation.

Suggested Citation

  • James O. Chipperfield & Christine M. O'Keefe, 2014. "Disclosure-protected Inference Using Generalised Linear Models," International Statistical Review, International Statistical Institute, vol. 82(3), pages 371-391, December.
    • Handle: RePEc:bla:istatr:v:82:y:2014:i:3:p:371-391
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    File URL: http://hdl.handle.net/10.1111/insr.12054
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    Cited by:

    1. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    2. Bernard Baffour & James Raymer & Ann Evans, 2023. "Recent Trends in Immigrant Fertility in Australia," Journal of International Migration and Integration, Springer, vol. 24(1), pages 47-67, March.
    3. Chipperfield James & Newman John & Thompson Gwenda & Ma Yue & Lin Yan-Xia, 2019. "Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server," Journal of Official Statistics, Sciendo, vol. 35(2), pages 319-336, June.

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