File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Privacy-Preserving Clustering with High Accuracy and Low Time Complexity

TitlePrivacy-Preserving Clustering with High Accuracy and Low Time Complexity
Authors
Issue Date2009
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 14th International Conference on Database Systems for Advanced Applications (DASFAA 2009), Brisbane, Australia, 21-23 April 2009. In Lecture Notes in Computer Science, 2009, v. 5463, p. 456-470 How to Cite?
AbstractThis paper proposes an effficient solution with high accuracy to the problem of privacy-preserving clustering. This problem has been studied mainly using two approaches: data perturbation and secure multiparty computation. In our research, we focus on the data perturbation approach, and propose an algorithm of linear time complexity based on 1-d clustering to perturb the data. Performance study on real datasets from the UCI machine learning repository shows that our approach reaches better accuracy and hence lowers the distortion of clustering result than previous approaches.
Persistent Identifierhttp://hdl.handle.net/10722/61178
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorCui, Yen_HK
dc.contributor.authorWong, WK-
dc.contributor.authorCheung, DWL-
dc.date.accessioned2010-07-13T03:32:36Z-
dc.date.available2010-07-13T03:32:36Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 14th International Conference on Database Systems for Advanced Applications (DASFAA 2009), Brisbane, Australia, 21-23 April 2009. In Lecture Notes in Computer Science, 2009, v. 5463, p. 456-470en_HK
dc.identifier.isbn9783642008863-
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61178-
dc.description.abstractThis paper proposes an effficient solution with high accuracy to the problem of privacy-preserving clustering. This problem has been studied mainly using two approaches: data perturbation and secure multiparty computation. In our research, we focus on the data perturbation approach, and propose an algorithm of linear time complexity based on 1-d clustering to perturb the data. Performance study on real datasets from the UCI machine learning repository shows that our approach reaches better accuracy and hence lowers the distortion of clustering result than previous approaches.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 Scienceen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.titlePrivacy-Preserving Clustering with High Accuracy and Low Time Complexityen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DWL=rp00101en_HK
dc.identifier.doi10.1007/978-3-642-00887-0_40en_HK
dc.identifier.scopuseid_2-s2.0-67650151258-
dc.identifier.hkuros164471en_HK
dc.identifier.volume5463en_HK
dc.identifier.spage456en_HK
dc.identifier.epage470en_HK
dc.publisher.placeGermanyen_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats