Privacy-Preserving k-NN for Small and Large Data Sets

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Title Privacy-Preserving k-NN for Small and Large Data Sets
Author Amirbekyan, Artak; Estivill-Castro, Vladimir
Publication Title Proceedings : ICDM Workshops 2007 : Seventh IEEE International Conference on Data Mining - Workshops (ICDMW 2007)
Editor Anthony K. H. Tung, Qiuming Zhu, Naren Ramakrishnan, osmar R. Zaiane, Yong Shi, Christopher W. Clift
Year Published 2007
Place of publication Washington, DC, USA
Publisher IEEE Computer Society
Abstract 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 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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.ieee.org/
Alternative URI http://www.ist.unomaha.edu/icdm2007/
Copyright Statement 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.
ISBN 0769530338
Conference name Seventh IEEE International Conference on Data Mining - Workshops (ICDMW 2007)
Location Omaha, USA
Date From 2007-10-28
Date To 2007-10-31
URI http://hdl.handle.net/10072/17250
Date Accessioned 2008-02-08
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Data Security
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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