Private Mining of Association Rules

  • Justin Zhan
  • Stan Matwin
  • LiWu Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)


This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parties involved in a data mining task. We study how to share private or confidential data in the following scenario: two parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for two parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning the association rules.


Privacy security association rule mining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Justin Zhan
    • 1
  • Stan Matwin
    • 1
  • LiWu Chang
    • 2
  1. 1.School of Information Technology & EngineeringUniversity of OttawaCanada
  2. 2.Center for High Assurance Computer SystemsNaval Research LaboratoryUSA

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