Abstract
Recently, there have been increasing interests on how to preserve the privacy in data mining when source of data are distributed across multi parties. In this paper, we focus on the privacy preserving on decision tree in multi party environment when data are vertically partitioned. We propose novel private decision tree algorithms applied to building and classification stages. The main advantage of our work over the existing ones is that each party cannot use the public decision tree to infer the other’s private data. With our algorithms, the communication cost during tree building stage is reduced compared to existing methods and the number of involving parties could be extended to be more than two parties.
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© 2005 Springer-Verlag Berlin Heidelberg
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Suthampan, E., Maneewongvatana, S. (2005). Privacy Preserving Decision Tree in Multi Party Environment. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_75
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DOI: https://doi.org/10.1007/11562382_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29186-2
Online ISBN: 978-3-540-32001-2
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