DocumentCode
3260581
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
fYear
2006
fDate
Dec. 2006
Firstpage
502
Lastpage
506
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location
Hong Kong
Print_ISBN
0-7695-2702-7
Type
conf
DOI
10.1109/ICDMW.2006.8
Filename
4063679
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