Privacy-preserving clustering with distributed EM mixture modeling
Author
Xiaodong Lin, Chris Clifton, Michael Zhu
Tech report number
CERIAS TR 2005-115
Abstract
Privacy and security considerations can prevent sharing of data, derailing data mining
projects. Distributed knowledge discovery can alleviate this problem. We present a technique
that uses EM mixture modeling to perform clustering on distributed data. This method controls
data sharing, preventing disclosure of individual data items or any results that can be traced to
an individual site.
Booktitle
Knowledge and Information Systems
Key alpha
Privacy; Security; Clustering
Publisher
Springer-Verlag London Ltd
Publication Date
2005-01-01
Copyright
Springer-Verlag London Ltd 2004
Keywords
Privacy; Security; Clustering