Privacy preserving clustering on horizontally partitioned data

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Official URL: http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=1623890&isnumber=34089&punumber=10810&k2dockey=1623890@ieeecnfs&query=%28levi+%3Cin%3E+au%29+%3Cand%3E+%2810810+%3Cin%3E+punumber%29&pos=0

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.