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End User Licence to Open Government Data? A Simulated Penetration Attack on Two Social Survey Datasets

Author

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  • Elliot Mark

    (School of Social Sciences, University of Manchester, Manchester M13 9PL, UK.)

  • Mackey Elaine

    (School of Social Sciences, University of Manchester, Manchester M13 9PL, UK.)

  • O’Shea Susan

    (School of Social Sciences, University of Manchester, Manchester M13 9PL, UK.)

  • Tudor Caroline

    (Office for National Statistics, Segensworth Road, Titchfield, Fareham, Hampshire, PO15 5RR, UK.)

  • Spicer Keith

    (Office for National Statistics, Segensworth Road, Titchfield, Fareham, Hampshire, PO15 5RR, UK.)

Abstract

In the UK, the transparency agenda is forcing data stewardship organisations to review their dissemination policies and to consider whether to release data that is currently only available to a restricted community of researchers under licence as open data. Here we describe the results of a study providing evidence about the risks of such an approach via a simulated attack on two social survey datasets. This is also the first systematic attempt to simulate a jigsaw identification attack (one using a mashup of multiple data sources) on an anonymised dataset. The information that we draw on is collected from multiple online data sources and purchasable commercial data. The results indicate that such an attack against anonymised end user licence (EUL) datasets, if converted into open datasets, is possible and therefore we would recommend that penetration tests should be factored into any decision to make datasets (that are about people) open.

Suggested Citation

  • Elliot Mark & Mackey Elaine & O’Shea Susan & Tudor Caroline & Spicer Keith, 2016. "End User Licence to Open Government Data? A Simulated Penetration Attack on Two Social Survey Datasets," Journal of Official Statistics, Sciendo, vol. 32(2), pages 329-348, June.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:329-348:n:7
    DOI: 10.1515/jos-2016-0019
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    References listed on IDEAS

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    1. Judit Bar‐Ilan & Kevin Keenoy & Mark Levene & Eti Yaari, 2009. "Presentation bias is significant in determining user preference for search results—A user study," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 135-149, January.
    2. Walter Mãœller & Uwe Blien & Heike Wirth, 1995. "Identification Risks of Microdata," Sociological Methods & Research, , vol. 24(2), pages 131-157, November.
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