Privacy preserving decision tree in multi party environment

Authors:
Eakalak Suthampan;Songrit Maneewongvatana
Affiliations:
Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand;Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Venue:
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Year:
2005

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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.