Privacy-Preserving Decision Tree Mining Based on Random Substitutions

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 fundamentall...

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Bibliographic Details
Published inEmerging Trends in Information and Communication Security pp. 145 - 159
Main Authors Dowd, Jim, Xu, Shouhuai, Zhang, Weining
Format Book Chapter
LanguageEnglish
Japanese
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
data mining
decision tree
matrix
perturbation
Privacy-preservation
Online AccessGet full text
ISBN9783540346401
3540346406
ISSN0302-9743
1611-3349
DOI10.1007/11766155_11

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Abstract 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.
AbstractList 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.
Author Xu, Shouhuai
Dowd, Jim
Zhang, Weining
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Notes This work was supported in part by US NFS grant IIS-0524612.
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Snippet Privacy-preserving decision tree mining is an important problem that has yet to be thoroughly understood. In fact, the privacy-preserving decision tree mining...
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StartPage 145
SubjectTerms data mining
decision tree
matrix
perturbation
Privacy-preservation
Title Privacy-Preserving Decision Tree Mining Based on Random Substitutions
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