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TOWARDS A BAYESIAN PERSPECTIVE ON STATISTICAL DISCLOSURE LIMITATION
Citation:
Cox, L H. TOWARDS A BAYESIAN PERSPECTIVE ON STATISTICAL DISCLOSURE LIMITATION. Presented at Sixth World Meeting of the International Society for Bayesian Analysis 2000, Hersonissos Heraklion-Crete, Greece, May 28-June 1, 2000.
Description:
National statistical offices and other organizations collect data on individual subjects (person, businesses, organizations), typically while assuring the subject that data pertaining to them will be held confidential. These data provide the raw material for statistical data products (tabular summaries, microdata files comprising data records pertaining to individual subjects, and, potentially, public statistical data bases and statistical query systems) which the statistical office disseminates to multiple, broad user communities. Statistical disclosure limitation (SDL) refers to the problem and methods for thwarting reidentification of a subject and divulging the subject's confidential data through analysis or manipulation of disseminated data products. SDL methods abbreviate or modify the data product sufficiently to thwart disclosure. SDL problems are typically computationally demanding; several have been shown to be NP-hard. Many SDL methods draw upon statistical, mathematical or optimization theory, but at the same time heuristic and partial approaches abound. Contributions from a Bayesian perspective have been few but are increasing. A strong theoretical connection between definitions of statistical disclosure, measurement of disclosure risk, and evaluation of SDL methods is lacking. This suggests opportunities for Bayesian and hierarchical approaches. Selected opportunities and associated SDL methodological issues are discussed.
The information in this article has been funded wholly or in part by the United States Environmental Protection Agency. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.