Privacy-preserving multi-party decision tree induction
by Justin Z. Zhan, Stan Matwin, LiWu Chang
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 2, 2007

Abstract: Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, sometimes the data are distributed among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties can collaboratively conduct data mining without breaching data privacy presents a grand challenge. In this paper, we propose a randomisation-based scheme for multi-parties to conduct data mining computations without disclosing their actual data sets to each other.

Online publication date: Mon, 04-Jun-2007

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