Secure Computation of the Mean and Related Statistics

  • Eike Kiltz
  • Gregor Leander
  • John Malone-Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3378)


In recent years there has been massive progress in the development of technologies for storing and processing of data. If statistical analysis could be applied to such data when it is distributed between several organisations, there could be huge benefits. Unfortunately, in many cases, for legal or commercial reasons, this is not possible.

The idea of using the theory of multi-party computation to analyse efficient algorithms for privacy preserving data-mining was proposed by Pinkas and Lindell. The point is that algorithms developed in this way can be used to overcome the apparent impasse described above: the owners of data can, in effect, pool their data while ensuring that privacy is maintained.

Motivated by this, we describe how to securely compute the mean of an attribute value in a database that is shared between two parties. We also demonstrate that existing solutions in the literature that could be used to do this leak information, therefore underlining the importance of applying rigorous theoretical analysis rather than settling for ad hoc techniques.


Secure Computation Leak Information Oblivious Transfer Privacy Preserve Data Mining Oblivious Transfer Protocol 
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 2005

Authors and Affiliations

  • Eike Kiltz
    • 1
    • 3
  • Gregor Leander
    • 1
  • John Malone-Lee
    • 2
  1. 1.Fakultät für MathematikRuhr-Universität BochumBochumGermany
  2. 2.Department of Computer ScienceUniversity of BristolBristolUK
  3. 3.University of Southern California at San DiegoLa JollaUSA

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