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Conference Paper: Protecting privacy in incremental maintenance for distributed association rule mining

TitleProtecting privacy in incremental maintenance for distributed association rule mining
Authors
Issue Date2008
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2008, v. 5012 LNAI, p. 381-392 How to Cite?
AbstractDistributed association rule mining algorithms are used to discover important knowledge from databases. Privacy concerns can prevent parties from sharing the data. New algorithms are required to solve traditional mining problems without disclosing (original or derived) information of their own data to other parties. Research results have been developed on (i) incrementally maintaining the discovered association rules, and (ii) computing the distributed association rules while preserving privacy. However, no study has been conducted on the problem of the maintenance of the discovered rules with privacy protection when new sites join the old sites. We propose an algorithm SIMDAR for this problem. Some techniques we developed can even further reduce the cost in a normal association rule mining algorithm with privacy protection. Experimental results showed that SIMDAR can significantly reduce the workload at the old sites by up to 80%. © 2008 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/93055
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorWong, WKen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorHung, Een_HK
dc.contributor.authorLiu, Hen_HK
dc.date.accessioned2010-09-25T14:49:32Z-
dc.date.available2010-09-25T14:49:32Z-
dc.date.issued2008en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2008, v. 5012 LNAI, p. 381-392en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93055-
dc.description.abstractDistributed association rule mining algorithms are used to discover important knowledge from databases. Privacy concerns can prevent parties from sharing the data. New algorithms are required to solve traditional mining problems without disclosing (original or derived) information of their own data to other parties. Research results have been developed on (i) incrementally maintaining the discovered association rules, and (ii) computing the distributed association rules while preserving privacy. However, no study has been conducted on the problem of the maintenance of the discovered rules with privacy protection when new sites join the old sites. We propose an algorithm SIMDAR for this problem. Some techniques we developed can even further reduce the cost in a normal association rule mining algorithm with privacy protection. Experimental results showed that SIMDAR can significantly reduce the workload at the old sites by up to 80%. © 2008 Springer-Verlag Berlin Heidelberg.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleProtecting privacy in incremental maintenance for distributed association rule miningen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-68125-0_34en_HK
dc.identifier.scopuseid_2-s2.0-44649113751en_HK
dc.identifier.hkuros149703en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44649113751&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5012 LNAIen_HK
dc.identifier.spage381en_HK
dc.identifier.epage392en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridWong, WK=8835876000en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridHung, E=7004256336en_HK
dc.identifier.scopusauthoridLiu, H=7409751811en_HK

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