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Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server

Author

Listed:
  • Chipperfield James
  • Newman John
  • Thompson Gwenda

    (Australian Bureau of Statistics, P.O. Box 10, Belconnen, Australian Capital Territory 2616, Australia.)

  • Ma Yue
  • Lin Yan-Xia

    (University of Wollongong, Wollongong, New South Wales 2522, Australia.)

Abstract

Many statistical agencies face the challenge of maintaining the confidentiality of respondents while providing as much analytical value as possible from their data. Datasets relating to businesses present particular difficulties because they are likely to contain information about large enterprises that dominate industries and may be more easily identified. Agencies therefore tend to take a cautious approach to releasing business data (e.g., trusted access, remote access and synthetic data). The Australian Bureau of Statistics has developed a remote server, called TableBuilder, which has the capability to allow users to specify and request tables created from business microdata. The tables are confidentialised automatically by perturbing cell values, and the results are returned quickly to the users. The perturbation method is designed to protect against attacks, which are attempts to undo the confidentialisation, such as the well-known differencing attack. This paper considers the risk and utility trade-off when releasing three Australian Bureau of Statistics business collections via its TableBuilder product.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:offsta:v:35:y:2019:i:2:p:319-336:n:2
    DOI: 10.2478/jos-2019-0015
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    References listed on IDEAS

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    1. Christine M. O'Keefe & James O. Chipperfield, 2013. "A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems," International Statistical Review, International Statistical Institute, vol. 81(3), pages 426-455, December.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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