DocumentCode :
3260700
Title :
A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining
Author :
Zhan, Justin ; Matwin, Stan
Author_Institution :
Heinz Sch., Carnegie Mellon Univ., Pittsburg, PA
fYear :
2006
fDate :
Dec. 2006
Firstpage :
546
Lastpage :
550
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
Keywords :
cryptography; data mining; data privacy; pattern classification; collaborative data mining; crypto based approach; k-nearest neighbor classification; privacy preservation; Collaboration; Cryptography; Data mining; Data privacy; Data security; Delta modulation; Information security; Information technology; Protection; Protocols;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
Type :
conf
DOI :
10.1109/ICDMW.2006.3
Filename :
4063687
Link To Document :
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