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Bayesian disclosure risk assessment: predicting small frequencies in contingency tables

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  • Jonathan J. Forster
  • Emily L. Webb

Abstract

Summary. We propose an approach for assessing the risk of individual identification in the release of categorical data. This requires the accurate calculation of predictive probabilities for those cells in a contingency table which have small sample frequencies, making the problem somewhat different from usual contingency table estimation, where interest is generally focused on regions of high probability. Our approach is Bayesian and provides posterior predictive probabilities of identification risk. By incorporating model uncertainty in our analysis, we can provide more realistic estimates of disclosure risk for individual cell counts than are provided by methods which ignore the multivariate structure of the data set.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssc:v:56:y:2007:i:5:p:551-570
    DOI: 10.1111/j.1467-9876.2007.00591.x
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    Cited by:

    1. 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.
    2. Duncan Smith, 2020. "Re‐identification in the Absence of Common Variables for Matching," International Statistical Review, International Statistical Institute, vol. 88(2), pages 354-379, August.
    3. Cinzia Carota & Maurizio Filippone & Silvia Polettini, 2022. "Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation," International Statistical Review, International Statistical Institute, vol. 90(1), pages 165-183, April.
    4. Natalie Shlomo & Chris Skinner, 2022. "Measuring risk of re‐identification in microdata: State‐of‐the art and new directions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1644-1662, October.

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