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Bayesian disclosure risk assessment: predicting small frequencies in contingency tables

Record type: Article

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

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Citation

Forster, Jonathan J. and Webb, Emily L. (2007) Bayesian disclosure risk assessment: predicting small frequencies in contingency tables Journal of the Royal Statistical Society. Series C: Applied Statistics, 56, (5), pp. 551-570. (doi:10.1111/j.1467-9876.2007.00591.x).

More information

Published date: November 2007
Organisations: Statistics, Southampton Statistical Research Inst.

Identifiers

Local EPrints ID: 46339
URI: http://eprints.soton.ac.uk/id/eprint/46339
ISSN: 0035-9254
PURE UUID: ebd3f836-9251-4c6f-93ec-62f4d8928563

Catalogue record

Date deposited: 19 Jun 2007
Last modified: 17 Jul 2017 15:08

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