Title :
A Max-Min Approach for Hiding Frequent Itemsets
Author :
Moustakides, George V. ; Verykios, Vassilios S.
Author_Institution :
Dept. of Comput. & Commun. Eng., Thessaly Univ., Volos
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
Keywords :
data encapsulation; data mining; minimax techniques; border theory; data mining; data sanitation; frequent itemset hiding; max-min approach; Association rules; Context; Data engineering; Data mining; Data privacy; Databases; Decision theory; Itemsets; Knowledge engineering; Performance evaluation;
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
DOI :
10.1109/ICDMW.2006.8
