A Practical Differentially Private Random Decision Tree Classifier

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A Practical Differentially Private Random Decision Tree Classifier
In this paper, we study the problem of constructing private classifiers using decision trees, within the framework of differential privacy. We first construct privacy-preserving ID3 decision trees using differentially private sum queries. Our experiments show that for many data sets a reasonable privacy guarantee can only be obtained via this method at a steep cost of accuracy in predictions. We then present a differentially private decision tree ensemble algorithm using the random decision tree approach. We demonstrate experimentally that our approach yields good prediction accuracy even when the size of the datasets is small. We also present a differentially private algorithm for the situation in which new data is periodically appended to an existing database. Our experiments show that our differentially private random decision tree classifier handles data updates in a way that maintains the same level of privacy guarantee.
Geetha Jagannathan, Krishnan Pillaipakkamnatt, Reb
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICDM
Authors Geetha Jagannathan, Krishnan Pillaipakkamnatt, Rebecca N. Wright
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