On Private Scalar Product Computation for Privacy-Preserving Data Mining

  • Bart Goethals
  • Sven Laur
  • Helger Lipmaa
  • Taneli Mielikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3506)

Abstract

In mining and integrating data from multiple sources, there are many privacy and security issues. In several different contexts, the security of the full privacy-preserving data mining protocol depends on the security of the underlying private scalar product protocol. We show that two of the private scalar product protocols, one of which was proposed in a leading data mining conference, are insecure. We then describe a provably private scalar product protocol that is based on homomorphic encryption and improve its efficiency so that it can also be used on massive datasets.

Keywords

Privacy-preserving data mining private scalar product protocol vertically partitioned frequent pattern mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bart Goethals
    • 1
  • Sven Laur
    • 2
  • Helger Lipmaa
    • 2
  • Taneli Mielikäinen
    • 1
  1. 1.HIIT Basic Research Unit, Department of Computer ScienceUniversity of HelsinkiFinland
  2. 2.Laboratory for Theoretical Computer Science,Department of Computer Science and EngineeringHelsinki University of TechnologyFinland

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