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Accelerating the Secure Distributed Computation of the Mean by a Chebyshev Expansion

  • Peter Lory
  • Manuel Liedel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7372)

Abstract

Lindell and Pinkas (2002) have proposed the idea of using the techniques of secure multi-party computations to generate efficient algorithms for privacy preserving data-mining. In this context Kiltz, Leander, and Malone-Lee (2005) have presented a protocol for the secure distributed computation of the mean and related statistics in a two-party setting. Their protocol achieves constant round complexity. As a novel suggestion we use a Chebyshev expansion to accelerate this protocol. This approach considerably reduces the overhead of the protocol in terms of both computation and communication. The proposed technique can be applied to other protocols in the field of privacy preserving data-mining as well.

Keywords

Privacy preserving data mining secure two-party computations oblivious polynomial evaluation Chebyshev expansion 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Peter Lory
    • 1
  • Manuel Liedel
    • 1
  1. 1.University of RegensburgRegensburgGermany

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