The Community for Technology Leaders
RSS Icon
Subscribe
Atlanta, Georgia
Apr. 3, 2006 to Apr. 7, 2006
ISBN: 0-7695-2571-7
pp: 95
Ali Inan , Sabanci University, Turkey
Y? Sayg? , Sabanci University, Turkey
Erkay Savas , Sabanci University, Turkey
Ay?a Azg? Hintoglu , Sabanci University, Turkey
Albert Levi , Sabanci University, Turkey
ABSTRACT
Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. The power of data mining tools to extract hidden information that cannot be otherwise seen by simple querying proved to be useful. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the privacy of individuals. Privacy preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a privacy preserving manner which can be used for privacy preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites.
INDEX TERMS
null
CITATION
Ali Inan, Y? Sayg?, Erkay Savas, Ay?a Azg? Hintoglu, Albert Levi, "Privacy Preserving Clustering on Horizontally Partitioned Data", ICDEW, 2006, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW) 2006, pp. 95, doi:10.1109/ICDEW.2006.115
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool