[Resource Topic] 2004/359: Secure Computation of the Mean and Related Statistics

Welcome to the resource topic for 2004/359

Secure Computation of the Mean and Related Statistics

Authors: Eike Kiltz, Gregor Leander, John Malone-Lee


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.

ePrint: https://eprint.iacr.org/2004/359

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