International Journal of Computer Applications in Technology
Efficient privacy preserving distributed clustering based on secret sharing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
APHID: An architecture for private, high-performance integrated data mining
Future Generation Computer Systems
Privacy preserving spatio-temporal clustering on horizontally partitioned data
Ubiquitous knowledge discovery
Privacy preserving spatio-temporal clustering on horizontally partitioned data
Ubiquitous knowledge discovery
Privacy preserving spatio-temporal clustering on horizontally partitioned data
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Collusion-Free Privacy Preserving Data Mining
International Journal of Intelligent Information Technologies
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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.