Show simple item record

dc.contributor.authorAmirbekyan, A
dc.contributor.authorEstivill-Castro, V
dc.date.accessioned2017-05-03T14:15:56Z
dc.date.available2017-05-03T14:15:56Z
dc.date.issued2007
dc.date.modified2008-05-26T02:08:24Z
dc.identifier.isbn9780769530192
dc.identifier.issn1550-4786
dc.identifier.doi10.1109/ICDMW.2007.67
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent293333 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.publisher.placeWashington, DC, USA
dc.publisher.urihttp://www.ieee.org/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameSeventh IEEE International Conference on Data Mining - Workshops (ICDMW 2007)
dc.relation.ispartofconferencetitleProceedings - IEEE International Conference on Data Mining, ICDM
dc.relation.ispartofdatefrom2007-10-28
dc.relation.ispartofdateto2007-10-31
dc.relation.ispartoflocationOmaha, USA
dc.relation.ispartofpagefrom699
dc.relation.ispartofpageto704
dc.rights.retentionY
dc.subject.fieldofresearchcode280505
dc.titlePrivacy-Preserving k-NN for Small and Large Data Sets
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.
gro.date.issued2007
gro.hasfulltextFull Text
gro.griffith.authorEstivill-Castro, Vladimir
gro.griffith.authorAmirbekyan, Artak


Files in this item

This item appears in the following Collection(s)

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

Show simple item record