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dc.contributor.authorAmirbekyan, Artaken_US
dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.contributor.editorJames Bailey and Alan Feketeen_US
dc.date.accessioned2017-05-03T14:15:55Z
dc.date.available2017-05-03T14:15:55Z
dc.date.issued2007en_US
dc.date.modified2008-11-06T23:04:17Z
dc.identifier.urihttp://hdl.handle.net/10072/17248
dc.description.abstractRecently, privacy issues have become important in clustering analysis, especially when data is horizontally partitioned over several parties. Associative queries are the core retrieval operation for many data mining algorithms, especially clustering and k-NN classification. The algorithms that effciently support k-NN queries are of special interest. We show how to adapt well-known data structures to the privacy preserving context and what is the overhead of this adaptation. We present an algorithm for k-NN in secure multiparty computation. This is based on presenting private computation of several metrics. As a result, we can offer three approaches to associative queries over horizontally partitioned data with progressively less security. We show privacy preserving algorithms for data structures that induce a partition on the space; such as KD-Trees. Our next preference is our Privacy Preserving SASH. However, we demonstrate that the most effective approach to achieve privacy is separate data structures for parties, where associative queries work separately, followed by secure combination to produce the overall output. This idea not only enhances security but also reduces communication cost between data holders. Our results and protocols also enable us to improve on previous approaches for k-NN classificationen_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent257374 bytes
dc.format.extent61704 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAustralian Computer Societyen_US
dc.publisher.placeBallarat, Australiaen_US
dc.publisher.urihttp://www.acs.org.au/en_US
dc.publisher.urihttp://www.se.auckland.ac.nz/acsc07/en_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameEighteenth Australasian Database Conference (ADC 2007)en_US
dc.relation.ispartofconferencetitleDatabase Technologies 2007 : Proceedings of the Eighteenth Australasian Database Conference (ADC2007)en_US
dc.relation.ispartofdatefrom2007-01-30en_US
dc.relation.ispartofdateto2007-02-02en_US
dc.relation.ispartoflocationBallarat, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280505en_US
dc.titleThe Privacy of k-NN Retrieval for Horizontal Partitioned Data - New Methods and Applicationsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2007 Australian Computer Society Inc. The attached file is reproduced here in accordance with the copyright policy of the publisher. Use hypertext link for access to the conference website.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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