A practical differentially private random decision tree classifier

Geetha Jagannathan, Krishnan Pillaipakkamnatt, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

103 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages114-121
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country/TerritoryUnited States
CityMiami, FL
Period12/6/0912/6/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Keywords

  • Classifiers
  • Differential privacy
  • Ensembles

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