Privacy-Preserving Decision Tree Mining Based on Random Substitutions

• Published in: Emerging Trends in Information and Communication Security pp. 145 - 159
• Main Authors: Dowd, Jim, Xu, Shouhuai, Zhang, Weining
• Format: Book Chapter
• Language: English; Japanese
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006
• Series: Lecture Notes in Computer Science
• Subjects: data mining; decision tree; matrix; perturbation; Privacy-preservation
• ISBN: 9783540346401; 3540346406
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11766155_11

Privacy-preserving decision tree mining is an important problem that has yet to be thoroughly understood. In fact, the privacy-preserving decision tree mining method explored in the pioneer paper [1] was recently showed to be completely broken, because its data perturbation technique is fundamentally flawed [2]. However, since the general framework presented in [1] has some nice and useful features in practice, it is natural to ask if it is possible to rescue the framework by, say, utilizing a different data perturbation technique. In this paper, we answer this question affirmatively by presenting such a data perturbation technique based on random substitutions. We show that the resulting privacy-preserving decision tree mining method is immune to attacks (including the one introduced in [2]) that are seemingly relevant. Systematic experiments show that it is also effective.