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Measuring risk of re-identification in microdata: state-of-the art and new directions

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  • Shlomo, Natalie
  • Skinner, Chris

Abstract

We review the influential research carried out by Chris Skinner in the area of statistical disclosure control, and in particular quantifying the risk of re-identification in sample microdata from a random survey drawn from a finite population. We use the sample microdata to infer population parameters when the population is unknown, and estimate the risk of re-identification based on the notion of population uniqueness using probabilistic modelling. We also introduce a new approach to measure the risk of re-identification for a subpopulation in a register that is not representative of the general population, for example a register of cancer patients. In addition, we can use the additional information from the register to measure the risk of re-identification for the sample microdata. This new approach was developed by the two authors and is published here for the first time. We demonstrate this approach in an application study based on UK census data where we can compare the estimated risk measures to the known truth.

Suggested Citation

  • Shlomo, Natalie & Skinner, Chris, 2022. "Measuring risk of re-identification in microdata: state-of-the art and new directions," LSE Research Online Documents on Economics 117168, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:117168
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    File URL: http://eprints.lse.ac.uk/117168/
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    References listed on IDEAS

    as
    1. Reiter, Jerome P., 2005. "Estimating Risks of Identification Disclosure in Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1103-1112, December.
    2. C. J. Skinner & M. J. Elliot, 2002. "A measure of disclosure risk for microdata," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 855-867, October.
    3. Skinner, Chris J. & Shlomo, Natalie, 2008. "Assessing identification risk in survey microdata using log-linear models," LSE Research Online Documents on Economics 39112, London School of Economics and Political Science, LSE Library.
    4. Shlomo, Natalie & Skinner, Chris J., 2010. "Assessing the protection provided by misclassification-based disclosure limitation methods for survey microdata," LSE Research Online Documents on Economics 39119, London School of Economics and Political Science, LSE Library.
    5. Paass, Gerhard, 1988. "Disclosure Risk and Disclosure Avoidance for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(4), pages 487-500, October.
    6. Jonathan J. Forster & Emily L. Webb, 2007. "Bayesian disclosure risk assessment: predicting small frequencies in contingency tables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(5), pages 551-570, November.
    7. Daniel Manrique-Vallier & Jerome P. Reiter, 2012. "Estimating Identification Disclosure Risk Using Mixed Membership Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1385-1394, December.
    8. Skinner, Chris & Shlomo, Natalie, 2008. "Assessing Identification Risk in Survey Microdata Using Log-Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 989-1001.
    9. C.J. Skinner, 1992. "On identification disclosure and prediction disclosure for microdata," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 46(1), pages 21-32, March.
    10. Duncan, George & Lambert, Diane, 1989. "The Risk of Disclosure for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 207-217, April.
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    More about this item

    Keywords

    disclosure risks; key variables; log-linear models; model specification; probability scores estimation; registers;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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