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Data Mining and Knowledge Discovery
Erschienen in: Data Mining and Knowledge Discovery 2/2005

01.09.2005

Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules*

verfasst von: Stephen E. Fienberg, Aleksandra B. Slavkovic

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 2/2005

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Abstract

In the statistical literature, there has been considerable development of methods of data releases for multivariate categorical data sets, where the releases come in the form of marginal tables corresponding to subsets of the categorical variables. Very recently some of the ideas have been extended to allow for the release of combinations of mixtures of marginal tables and conditional tables for subsets of variables. Association rules can be viewed as conditional tables. In this paper we consider possible inferences an intruder can make about confidential categorical data following the release of information on one or more association rules. We illustrate this with several examples.
Vorheriger Artikel A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
Nächster Artikel Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata

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Metadaten
Titel
Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules*
verfasst von
Stephen E. Fienberg
Aleksandra B. Slavkovic
Publikationsdatum
01.09.2005
Verlag
Springer US
Erschienen in
Data Mining and Knowledge Discovery / Ausgabe 2/2005
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-005-0010-x

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