Journal of Cryptology

, Volume 15, Issue 3, pp 177–206 | Cite as

Privacy Preserving Data Mining

  • Lindell
  • Pinkas


In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated by the need both to protect privileged information and to enable its use for research or other purposes.

The above problem is a specific example of secure multi-party computation and, as such, can be solved using known generic protocols. However, data mining algorithms are typically complex and, furthermore, the input usually consists of massive data sets. The generic protocols in such a case are of no practical use and therefore more efficient protocols are required. We focus on the problem of decision tree learning with the popular ID3 algorithm. Our protocol is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth.

Key words. Secure two-party computation, Oblivious transfer, Oblivious polynomial evaluation, Data mining, Decision trees. 


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Copyright information

© International Association for Cryptologic Research 2002

Authors and Affiliations

  • Lindell
    • 1
  • Pinkas
    • 2
  1. 1.Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel
  2. 2.STAR Lab, Intertrust Technologies, 4750 Patrick Henry Drive, Santa Clara, CA 95054, U.S.A. benny@pinkas.netUS

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