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Data Mining and Knowledge Discovery
Published 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*

Authors: Stephen E. Fienberg, Aleksandra B. Slavkovic

Published in: Data Mining and Knowledge Discovery | Issue 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.
previous article A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
next article Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata

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Metadata
Title
Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules*
Authors
Stephen E. Fienberg
Aleksandra B. Slavkovic
Publication date
01-09-2005
Publisher
Springer US
Published in
Data Mining and Knowledge Discovery / Issue 2/2005
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-005-0010-x

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