Private Mining of Association Rules

  • Justin Zhan
  • Stan Matwin
  • LiWu Chang
Conference paper

DOI: 10.1007/11427995_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)
Cite this paper as:
Zhan J., Matwin S., Chang L. (2005) Private Mining of Association Rules. In: Kantor P. et al. (eds) Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

Privacy security association rule mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations