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Confidentiality and linked data

Author

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  • Felix Ritchie
  • Jim Smith

Abstract

Data providers such as government statistical agencies perform a balancing act: maximising information published to inform decision-making and research, while simultaneously protecting privacy. The emergence of identified administrative datasets with the potential for sharing (and thus linking) offers huge potential benefits but significant additional risks. This article introduces the principles and methods of linking data across different sources and points in time, focusing on potential areas of risk. We then consider confidentiality risk, focusing in particular on the "intruder" problem central to the area, and looking at both risks from data producer outputs and from the release of micro-data for further analysis. Finally, we briefly consider potential solutions to micro-data release, both the statistical solutions considered in other contributed articles and non-statistical solutions.

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  • Felix Ritchie & Jim Smith, 2019. "Confidentiality and linked data," Papers 1907.06465, arXiv.org.
    • Handle: RePEc:arx:papers:1907.06465
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    References listed on IDEAS

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    1. Hans-Peter Hafner & Felix Ritchie & Rainer Lenz, 2015. "User-focused threat identification for anonymised microdata," Working Papers 20151503, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    2. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    3. 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.
    4. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    5. Felix Ritchie, 2014. "Operationalising ‘safe statistics’: the case of linear regression," Working Papers 20141410, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
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