Privacy-preserving multi-party decision tree induction. (5th June 2007)

• Record Type: Journal Article
• Authors: Zhan, Justin Z.; Matwin, Stan; Chang, LiWu
• Is Part Of: International journal of business intelligence and data mining. Volume 2:Number 2(2007)
• Page Start: 197
• Page End: 212
• Publication Date: 2007-06-05
• Subjects: data mining -- decision tree classification -- privacy preservation -- randomisation -- data privacy; 006.312
• Languages: English
• ISSNs: 1743-8187

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.