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Privacy-Preserving Graph Algorithms in the Semi-honest Model

  • Justin Brickell
  • Vitaly Shmatikov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3788)

Abstract

We consider scenarios in which two parties, each in possession of a graph, wish to compute some algorithm on their joint graph in a privacy-preserving manner, that is, without leaking any information about their inputs except that revealed by the algorithm’s output.

Working in the standard secure multi-party computation paradigm, we present new algorithms for privacy-preserving computation of APSD (all pairs shortest distance) and SSSD (single source shortest distance), as well as two new algorithms for privacy-preserving set union. Our algorithms are significantly more efficient than generic constructions. As in previous work on privacy-preserving data mining, we prove that our algorithms are secure provided the participants are “honest, but curious.”

Keywords

Secure Multiparty Computation Graph Algorithms Privacy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Justin Brickell
    • 1
  • Vitaly Shmatikov
    • 1
  1. 1.The University of Texas at AustinAustinUSA

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