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

Authors:
Stephen E. Fienberg;Aleksandra B. Slavkovic
Affiliations:
Department of Statistics, Cylab, and Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, USA 15213-3890;Department of Statistics, Pennsylvania State University, University Park, USA 16802
Venue:
Data Mining and Knowledge Discovery
Year:
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.