Author
V.Verykios, A.Elmagarmid, E.Dasseni, E. Bertino, Y.Saygin
Abstract
Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection
of the confidentiality of this information has been a long-term goal for the database security research community and for the
government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks
that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to
balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects,
in some way, and modifies true data values and relationships. In this paper, we investigate confidentiality issues of a broad category of
rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which
is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold.
Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive
data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in
order to analyze their time complexity and the impact that they have in the original database.
