Privacy-preserving boosting |
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Authors: | Sébastien Gambs Balázs Kégl Esma Aïmeur |
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Affiliation: | (1) Department of Computer Science and Operations Research, University of Montreal, C. P. 6128, Succ. Centre-Ville, Montréal, Québec, Canada, H3C 3J7 |
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Abstract: | We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing
their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the
excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set.
Responsible Editor: Charu Aggarwal. |
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Keywords: | Privacy-preserving data mining Boosting AdaBoost distributed learning" target="_blank">AdaBoost distributed learning Secure multiparty computation |
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