## 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.

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## Acknowledgments

We owe special thanks to Alan Karr for drawing our attention to the close correspondence between the confidentiality problems we have been working on and those associated with association rule mining. We are indebted to the comments of the referees for some references and suggestions that helped to emphasize the complementary nature of the statistical and datamining literatures. This research is part of several larger efforts focused on privacy and confidentiality, including a project coordinated by the National Institute of Statistical Sciences involving several U.S. federal statistical agencies.

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^{*}The research reported here was supported in part by NSF grants EIA–9876619 and IIS–0131884 to the National Institute of Statistical Sciences, as well as by Grant R01-AG023141 from the NIH to the Department of Statistics and by Army contract DAAD19-02-1-3-0389 to CyLab, both at Carnegie Mellon University.

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Fienberg, S.E., Slavkovic, A.B. Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules^{*}
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*Data Min Knowl Disc* **11**, 155–180 (2005). https://doi.org/10.1007/s10618-005-0010-x

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DOI: https://doi.org/10.1007/s10618-005-0010-x