Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Secure Multiparty Computation Methods

  • Murat KantarcoluEmail author
  • Jaideep Vaidya
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1388


The problem of preserving privacy while allowing data analysis can be attacked in many ways. One way is to avoid disclosing data beyond its source while still constructing data mining models equivalent to those that would have been learned on an integrated data set. This follows the approach of Secure Multiparty Computation (SMC). SMC refers to the general problem of computing a given function securely over private inputs while revealing nothing extra to any party except what can be inferred (in polynomial time) from its input and output. Since one can prove that data are not disclosed beyond its original source, the opportunity for misuse is not increased by the process of data mining.

The definition of privacy followed in this line of research is conceptually simple: no site should learn anything new from the processof data mining. Specifically, anything learned during the data mining process must be derivable given one’s own data and the final result. In other words,...

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Texas at DallasRichardsonUSA
  2. 2.Rutgers UniversityNewarkUSA

Section editors and affiliations

  • Chris Clifton
    • 1
  1. 1.Department of Computer SciencePurdue UniversityWest LafayetteUSA