Privacy-preserving similarity coefficients for binary data

Kok Seng Wong, Myung Ho Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Similarity coefficients (also known as coefficients of association) are important measurement techniques used to quantify the extent to which objects resemble one another. Due to privacy concerns, the data owner might not want to participate in any similarity measurement if the original dataset will be revealed or could be derived from the final output. There are many different measurements used for numerical, structural and binary data. In this paper, we particularly consider the computation of similarity coefficients for binary data. A large number of studies related to similarity coefficients have been performed. Our objective in this paper is not to design a specific similarity coefficient. Rather, we are demonstrating how to compute similarity coefficients in a secure and privacy preserved environment. In our protocol, a client and a server jointly participate in the computation. At the end of the protocol, the client will obtain all summation variables needed for the computation while the server learns nothing. We incorporate cryptographic methods in our protocol to protect the original dataset and all other intermediate results. Note that our protocol also supports dissimilarity coefficients.

Original languageEnglish
Pages (from-to)1280-1290
Number of pages11
JournalComputers and Mathematics with Applications
Issue number9
Publication statusPublished - May 1 2013
Externally publishedYes


  • Association coefficients
  • Binary data similarity measures
  • Privacy preserving similarity test
  • Similarity coefficients
  • Similarity computation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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