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dc.contributor.authorAmirbekyan, Artak
dc.contributor.authorEstivill-Castro, Vladimir
dc.contributor.editorXu, D
dc.contributor.editorGao, K
dc.contributor.editorLi, X
dc.contributor.editorHan, Z
dc.date.accessioned2017-05-03T14:15:57Z
dc.date.available2017-05-03T14:15:57Z
dc.date.issued2007
dc.date.modified2008-12-24T03:17:57Z
dc.identifier.isbn978-960-6766-05-3
dc.identifier.urihttp://hdl.handle.net/10072/17251
dc.description.abstractRegression is arguably the most applied data analysis method. Today there are many scenarios where data for attributes that correspond to predictor variables and the response variable itself are distributed among several parties that do not trust each other. Privacy-preserving data mining has grown rapidly studying the scenarios where data is vertically partitioned. While algorithms have been developed for many tasks (like clustering, association-rule mining and classification), for regression, the case of only two parties remains open. Also open is the most interesting case when the response variable is to be kept private. This paper provides the first set of algorithms that solves these cases. Our algorithms are practical and only require a commodity server (a supplier of random values) that does not collude with the parties. Our protocols are secure in the spirit of the the semi-honest model.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent178156 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherWorld Scientifica and Engineering Academy Society Press
dc.publisher.placeUniversity of Wisconsin, USA
dc.publisher.urihttps://www.wseas.org/cms.action
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename7th WSEAS International Conference on Simulation, Modelling and Optimization
dc.relation.ispartofconferencetitleNEW ADVANCES IN SIMULATION, MODELLING AND OPTIMIZATION (SMO '07)
dc.relation.ispartofdatefrom2007-09-15
dc.relation.ispartofdateto2007-09-17
dc.relation.ispartoflocationBeijing, PEOPLES R CHINA
dc.relation.ispartofpagefrom37
dc.relation.ispartofpagefrom3 pages
dc.relation.ispartofpageto+
dc.relation.ispartofpageto3 pages
dc.rights.retentionY
dc.subject.fieldofresearchcode230199
dc.titlePrivacy Preserving Regression Algorithms
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© The Author(s) 2007. The attached file is posted here with permission of the copyright owners for your personal use only. No further distribution permitted. For information about this conference please refer to the publisher's website or contact the authors.
gro.date.issued2007
gro.hasfulltextFull Text
gro.griffith.authorEstivill-Castro, Vladimir
gro.griffith.authorAmirbekyan, Artak


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

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