Estimating Identification Disclosure Risk Using Mixed Membership Models.

ABSTRACT: Statistical agencies and other organizations that disseminate data are obligated to protect data subjects' confidentiality. For example, ill-intentioned individuals might link data subjects to records in other databases by matching on common characteristics (keys). Successful links are particularly problematic for data subjects with combinations of keys that are unique in the population. Hence, as part of their assessments of disclosure risks, many data stewards estimate the probabilities that sample uniques on sets of discrete keys are also population uniques on those keys. This is typically done using log-linear modeling on the keys. However, log-linear models can yield biased estimates of cell probabilities for sparse contingency tables with many zero counts, which often occurs in datab

SUBMITTER: Manrique-Vallier D 

PROVIDER: S-EPMC4159106 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature