In this paper, we study the privacy-preserving decision tree building problem on vertically partitioned data. We made two contributions. First, we propose a novel hybrid approach, which takes advantage of the strength of the two existing approaches, randomization and the secure multi-party computation (SMC), to balance the accuracy and efficiency constraints. Compared to these two existing approaches, our proposed approach can achieve much better accuracy than randomization approach and much reduced computation cost than SMC approach. We also propose a multi-group scheme that makes it flexible for data miners to control the balance between data mining accuracy and privacy. We partition attributes into groups, and develop a scheme to conduct group-based randomization to achieve better data mining accuracy. We have implemented and evaluated the proposed schemes for the ID3 decision tree algorithm. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Teng, Z., & Du, W. (2007). A hybrid multi-group privacy-preserving approach for building decision trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4426 LNAI, pp. 296–307). Springer Verlag. https://doi.org/10.1007/978-3-540-71701-0_30
Mendeley helps you to discover research relevant for your work.