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Inference on Distributed Data Clustering

Josenildo Costa da Silva; Matthias Klusch
In: B. Grabot (Hrsg.). International Journal Engineering Applications of Artificial Intelligence, Vol. 19, No. 4, Pages 363-369, Elsevier Science Publishers B. V, 2006.


In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present KDEC-S algorithm for distributed data clustering, which is shown to provide mining results while preserving confidentiality of original data. We also present a confidentiality framework with which we can state the confidentiality level of KDEC-S. The underlying idea of KDEC-S is to use an approximation of density estimation such that the original data cannot be reconstructed to a given extent.