Filtering for Private Collaborative Benchmarking

  • Florian Kerschbaum
  • Orestis Terzidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3995)


Collaborative Benchmarking is an important issue for modern enterprises, but the business performance quantities used as input are often highly confidential. Secure Multi-Party Computation can offer protocols that can compute benchmarks without leaking the input variables. Benchmarking is a process of comparing to the “best”, so often it is necessary to only include the k-best enterprises for computing a benchmark to not distort the result with some outlying performances. We present a protocol that can be used as a filter, before running any collaborative benchmarking protocol that restricts the participants to the k best values. Our protocol doesn’t use the general circuit construction technique for SMC aiming to optimize performance. As building blocks we present the fastest implementation of Yao’s millionaires’ protocol and a protocol that achieves a fair shuffle in O(log n) rounds.


Encryption Scheme Random Permutation Homomorphic Encryption Oblivious Transfer Modular Exponentiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Florian Kerschbaum
    • 1
  • Orestis Terzidis
    • 1
  1. 1.SAP ResearchKarlsruheGermany

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