Secure Computation of the Mean and Related Statistics

Eike Kiltz; Gregor Leander; John Malone-Lee

Paper 2004/359

Secure Computation of the Mean and Related Statistics

Eike Kiltz, Gregor Leander, and John Malone-Lee

Abstract

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.

Metadata

Available format(s): PDF PS
Category: Cryptographic protocols
Publication info: Published elsewhere. An extended abstract will appear at TCC '05
Contact author(s): ekiltz @ cs ucsd edu
History: 2004-12-15: received
Short URL: https://ia.cr/2004/359
License: CC BY

BibTeX

@misc{cryptoeprint:2004/359,
      author = {Eike Kiltz and Gregor Leander and John Malone-Lee},
      title = {Secure Computation of the Mean and Related Statistics},
      howpublished = {Cryptology {ePrint} Archive, Paper 2004/359},
      year = {2004},
      url = {https://eprint.iacr.org/2004/359}
}