A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining

  • Authors:
  • Justin Zhan;Stan Matwin

  • Affiliations:
  • Carnegie Mellon University;University of Ottawa

  • Venue:
  • ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
  • Year:
  • 2006

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Abstract

To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. In this paper, we propose a formal definition of privacy, develop a solution for privacy-preserving k-nearest neighbor classification which is one of data mining tasks, and show that our solution preserves data privacy according to our definition.