Inference on Distributed Data Clustering

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.