Inference in distributed data clustering |
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| Affiliation: | 1. MRC Mitochondrial Biology Unit, Wellcome Trust/MRC Building, Cambridge CB2 0XY, UK;2. Department of Pathology, Centre for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA;3. Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK;4. Department of Chemistry, University of Otago, Dunedin, New Zealand;5. Centre for the Chemical Research of Ageing, WestCHEM School of Chemistry, University of Glasgow, Glasgow G12 8QQ, UK;1. Faculty of Psychology and Educational Science, University of Geneva, Geneva, Switzerland;2. Department of Neurology, University Hospitals of Geneva, Geneva, Switzerland;3. Department of Obstetrics and Gynecology, University Hospitals of Geneva, Geneva, Switzerland;1. Volatility Foundation, USA;2. Center for Computation and Technology, Louisiana State University, USA;3. School of Electrical Engineering & Computer Science, Louisiana State University, USA;1. Université Côte d’Azur, CNRS, Inserm, IBV, Nice, France |
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| Abstract: | 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. |
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