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has been cited by the following article:
TITLE: Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset
AUTHORS: Animesh Tripathy, Ipsa De
KEYWORDS: Data Mining; Privacy; Hierarchical; Clustering
JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.5B, October 12, 2013
ABSTRACT: Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems. However, this problem becomes more challenging when dataset is distributed between different parties and they do not want to share their data. So, in this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set. Each site only learns the final cluster centers, but nothing about the individual’s data.