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- Publisher Website: 10.1007/978-3-642-00887-0_40
- Scopus: eid_2-s2.0-67650151258
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Conference Paper: Privacy-Preserving Clustering with High Accuracy and Low Time Complexity
Title | Privacy-Preserving Clustering with High Accuracy and Low Time Complexity |
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Authors | |
Issue Date | 2009 |
Publisher | Springer 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/61178 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Cui, Y | en_HK |
dc.contributor.author | Wong, WK | - |
dc.contributor.author | Cheung, DWL | - |
dc.date.accessioned | 2010-07-13T03:32:36Z | - |
dc.date.available | 2010-07-13T03:32:36Z | - |
dc.date.issued | 2009 | en_HK |
dc.identifier.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 | en_HK |
dc.identifier.isbn | 9783642008863 | - |
dc.identifier.issn | 0302-9743 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/61178 | - |
dc.description.abstract | This 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.language | eng | en_HK |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | en_HK |
dc.relation.ispartof | Lecture Notes in Computer Science | en_HK |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.title | Privacy-Preserving Clustering with High Accuracy and Low Time Complexity | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | en_HK |
dc.identifier.authority | Cheung, DWL=rp00101 | en_HK |
dc.identifier.doi | 10.1007/978-3-642-00887-0_40 | en_HK |
dc.identifier.scopus | eid_2-s2.0-67650151258 | - |
dc.identifier.hkuros | 164471 | en_HK |
dc.identifier.volume | 5463 | en_HK |
dc.identifier.spage | 456 | en_HK |
dc.identifier.epage | 470 | en_HK |
dc.publisher.place | Germany | en_HK |
dc.identifier.issnl | 0302-9743 | - |