TY - JOUR
T1 - Preserving the confidentiality of categorical statistical data bases when releasing information for association rules
AU - Fienberg, Stephen E.
AU - Slavkovic, Aleksandra B.
N1 - Funding Information:
∗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.
PY - 2005/9
Y1 - 2005/9
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/26944471098
UR - https://www.scopus.com/pages/publications/26944471098#tab=citedBy
U2 - 10.1007/s10618-005-0010-x
DO - 10.1007/s10618-005-0010-x
M3 - Review article
AN - SCOPUS:26944471098
SN - 1384-5810
VL - 11
SP - 155
EP - 180
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 2
ER -