A MaxMin approach for hiding frequent itemsets

dc.creator	Moustakides, G. V.	en
dc.creator	Verykios, V. S.	en
dc.date.accessioned	2015-11-23T10:39:59Z
dc.date.available	2015-11-23T10:39:59Z
dc.date.issued	2008
dc.identifier	10.1016/j.datak.2007.06.012
dc.identifier.issn	0169-023X
dc.identifier.uri	http://hdl.handle.net/11615/31169
dc.description.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. Experimental results indicate the effectiveness of the proposed methodology both with respect to the hiding results as well as with respect to the time performance compared to similar state of the art approaches. (c) 2007 Elsevier B.V. All rights reserved.	en
dc.source	Data & Knowledge Engineering	en
dc.source.uri	<Go to ISI>://WOS:000254823400006
dc.subject	privacy preserving data mining	en
dc.subject	knowledge hiding	en
dc.subject	frequent itemset	en
dc.subject	hiding	en
dc.subject	association rule hiding	en
dc.subject	DISCOVERY	en
dc.subject	Computer Science, Artificial Intelligence	en
dc.subject	Computer Science, Information	en
dc.subject	Systems	en
dc.title	A MaxMin approach for hiding frequent itemsets	en
dc.type	journalArticle	en

Ficheros en el ítem

Ficheros	Tamaño	Formato	Ver
No hay ficheros asociados a este ítem.