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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 546-550
Justin Zhan , Carnegie Mellon University
Stan Matwin , University of Ottawa
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.
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CITATION

S. Matwin and J. Zhan, "A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 546-550.
doi:10.1109/ICDMW.2006.3
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