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Assessing Microdata Disclosure Risk Using the Poisson-Inverse Guassian Distribution
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2002 (English)In: Statistics In Transition, ISSN 1234-7655, Vol. 5, no 6, 901-925 p.Article in journal (Refereed) Published
Abstract [en]

An important measure of identification risk associated with the release of microdata or large complex tables is the number or proportion of population units that can be uniquely identified by some set of characterizing attributes which partition the population into subpopulations or cells. Various methods for estimating this quantity based on sample data have been proposed in the literature by means of superpopulation models. In the present paper the Poisson- inverse Gaussian (PiG) distribution is proposed as a possible approach within this context. Disclosure risk measures are discussed and derived under the proposed model as are various methods of estimation. An example on real data is given and the results indicate that the PiG model may be a useful alternative to other models.

Place, publisher, year, edition, pages
2002. Vol. 5, no 6, 901-925 p.
Keyword [en]
statistical disclosure, uniqueness, inverse-Gaussian, Poisson-mixture, superpopulation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-96476OAI: oai:DiVA.org:su-96476DiVA: diva2:666050
Available from: 2013-11-21 Created: 2013-11-21 Last updated: 2017-11-22Bibliographically approved

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CiteExportLink to record
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