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dc.contributor.authorAmirbekyan, Artaken_US
dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.contributor.editorEditor exceeds RIMS limiten_US
dc.date.accessioned2017-05-03T14:15:56Z
dc.date.available2017-05-03T14:15:56Z
dc.date.issued2007en_US
dc.date.modified2008-05-26T02:08:24Z
dc.identifier.doi10.1109/ICDMW.2007.67en_AU
dc.identifier.urihttp://hdl.handle.net/10072/17250
dc.description.abstractIt is not surprising that there is strong interest in k- NN queries to enable clustering, classification and outlier- detection tasks. However, previous approaches to privacy- preserving k-NN are costly and can only be realistically ap- plied to small data sets. We provide efficient solutions for k-NN queries queries for vertically partitioned data. We pro- vide the first solution for the L (or Chessboard) metric as well as detailed privacy-preserving computation of all other Minkowski metrics. We enable privacy-preserving L by providing a solution to the Yao's Millionaire Problem with more than two parties. This is based on a new and practi- cal solution to Yao's Millionaire with shares. We also provide privacy-preserving algorithms for combinations of local met- rics into a global that handles the large dimensionality and diversity of attributes common in vertically partitioned data.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent293333 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeWashington, DC, USAen_US
dc.publisher.urihttp://www.ieee.org/en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameSeventh IEEE International Conference on Data Mining - Workshops (ICDMW 2007)en_US
dc.relation.ispartofconferencetitleProceedings : ICDM Workshops 2007 : Seventh IEEE International Conference on Data Mining - Workshops (ICDMW 2007)en_US
dc.relation.ispartofdatefrom2007-10-28en_US
dc.relation.ispartofdateto2007-10-31en_US
dc.relation.ispartoflocationOmaha, USAen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280505en_US
dc.titlePrivacy-Preserving k-NN for Small and Large Data Setsen_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 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.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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