loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)
A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining
Hong Kong, China
December 18-December 22
ISBN: 0-7695-2702-7
Justin Zhan, Carnegie Mellon University
Stan Matwin, University of Ottawa
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.
Citation:
Justin Zhan, Stan Matwin, "A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining," icdmw, pp.546-550, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.