Abstract
In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of colluders in a distributed data mining group. We also present KDEC-S, which is a distributed clustering algorithm designed to provide mining results while preserving confidentiality of original data. The underlying idea of our algorithm is to use an approximation of density estimation such that it is not possible to reconstruct the original data with better probability than some given level.
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da Silva, J.C., Klusch, M. (2005). Inference on Distributed Data Clustering. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_60
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DOI: https://doi.org/10.1007/11510888_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
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