A Max-Min Approach for Hiding Frequent Itemsets

Authors:
George V. Moustakides;Vassilios S. Verykios
Affiliations:
University of Thessaly, Volos, GREECE;University of Thessaly, Volos, GREECE
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

In this paper we are proposing a new algorithmic approach for sanitizing raw data from sensitive knowledge in the context of mining of association rules. The new approach (a) relies on the maxmin criterion which is a method in decision theory for maximizing the minimum gain and (b) builds upon the border theory of frequent itemsets.