Bayesian disclosure risk assessment: predicting small frequencies in contingency tables

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), 551-570. (doi:10.1111/j.1467-9876.2007.00591.x).


Full text not available from this repository.


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

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/j.1467-9876.2007.00591.x
ISSNs: 0035-9254 (print)
Related URLs:
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 46339
Accepted Date and Publication Date:
November 2007Published
Date Deposited: 19 Jun 2007
Last Modified: 31 Mar 2016 12:21

Actions (login required)

View Item View Item