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- W151567798 abstract "Summary: When microdata Þles for research are released, it is possible that external users may attempt to breach conÞdentiality. For this reason most Statistical Agencies apply some form of disclosure risk assessment and data protection. Risk assessment Þrst requires a measure of disclosure risk to be deÞned. The deÞnition of disclosure risk that we adopt is based on re-identiÞcation. Therefore our risk measure is speciÞc to cells of the contingency table built by cross tabulating the variables that allow identiÞcation. In this paper we discuss two Bayesian hierarchical models for disclosure risk estimation. Model I is an extension due to Polettini and Stander (2004) of a model discussed by Bethlehem, Keller and Pannekoek (1990). Model II is an extension of Model I that takes account of the large number of empty cells and that makes use of the available estimate of sample cell probabilities based on sampling design weights. For each model we present in detail the distributions that are necessary for risk estimation. An artiÞcial sample of the Italian 1991 Census data allows us to assess the relative performance of each model." @default.
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- W151567798 date "2005-01-01" @default.
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- W151567798 title "On some Bayesian models for risk estimation in statistical disclosure limitation" @default.
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