Privacy preserving unsupervised clustering over vertically partitioned data

Authors:
D. K. Tasoulis;E. C. Laskari;G. C. Meletiou;M. N. Vrahatis
Affiliations:
,Computational Intelligence Laboratory, Department of Mathematics, University of Patras, Patras, Greece;,Computational Intelligence Laboratory, Department of Mathematics, University of Patras, Patras, Greece;,University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, Patras, Greece;,Computational Intelligence Laboratory, Department of Mathematics, University of Patras, Patras, Greece
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
Year:
2006

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Abstract

The exponential growth of databases containing personal information has rendered the task of extracting high quality information from collections of such databases very important. This task is hindered by the security concerns that arise, due to the confidentiality of the data records, and the reluctance of the organizations to disclose their data. This paper proposes a clustering algorithmic scheme that ensures privacy and confidentiality of the data without compromising the effectiveness of the clustering algorithm nor imposing high communication costs.