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  • Practical protocol for Yao’s millionaires problem enables secure multi-party computation of metrics and efficient privacy-preserving k-NN for large data sets

    Author(s)
    Amirbekyan, Artak
    Estivill-Castro, Vladimir
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Amirbekyan, Artak
    Year published
    2009
    Metadata
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    Abstract
    Finding 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 ...
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    Finding 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.
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    Journal Title
    Knowledge and Information Systems
    Volume
    21
    Issue
    3
    DOI
    https://doi.org/10.1007/s10115-009-0233-z
    Subject
    Distributed computing and systems software not elsewhere classified
    Information systems
    Publication URI
    http://hdl.handle.net/10072/30120
    Collection
    • Journal articles

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