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dc.contributor.authorAmirbekyan, Artaken_US
dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.date.accessioned2017-05-03T14:16:00Z
dc.date.available2017-05-03T14:16:00Z
dc.date.issued2009en_US
dc.date.modified2010-08-05T07:15:38Z
dc.identifier.issn02193116en_US
dc.identifier.doi10.1007/s10115-009-0233-zen_AU
dc.identifier.urihttp://hdl.handle.net/10072/30120
dc.description.abstractFinding the nearest k objects to a query object is a fundamental operation for many data mining algorithms. With the recent interest in privacy, it 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 have been costly and can only be realistically applied to small data sets. In this paper, we provide efficient solutions for k-NN queries for vertically partitioned data. We provide the first solution for the L(inf) (or Chessboard) metric as well as detailed privacy-preserving computation of all other Minkowski metrics. We enable privacy-preserving L(inf) by providing a practical approach to the Yao's millionaire problem with more than two parties. This is based on a pragmatic and implementable solution to Yao's millionaire problem with shares. We also provide privacy-preserving algorithms for combinations of local metrics into a global metric that handles the large dimensionality and diversity of attributes common in vertically partitioned data. To manage very large data sets, we provide a privacy-preserving SASH (a very successful data structure for associative queries in high dimensions). Besides providing a theoretical analysis, we illustrate the efficiency of our approach with an empirical evaluation.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom327en_US
dc.relation.ispartofpageto363en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalKnowledge and Information Systemsen_US
dc.relation.ispartofvolume21en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchAnalysis of Algorithms and Complexityen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchDistributed Computing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080201en_US
dc.subject.fieldofresearchcode080109en_US
dc.subject.fieldofresearchcode080599en_US
dc.titlePractical protocol for Yao’s millionaires problem enables secure multi-party computation of metrics and efficient privacy-preserving k-NN for large data setsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.date.issued2009
gro.hasfulltextNo Full Text


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