Mixture of gaussian models and bayes error under differential privacy
Proceedings of the first ACM conference on Data and application security and privacy
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Privately releasing conjunctions and the statistical query barrier
Proceedings of the forty-third annual ACM symposium on Theory of computing
Large margin multiclass gaussian classification with differential privacy
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
A Practical Differentially Private Random Decision Tree Classifier
Transactions on Data Privacy
Differential privacy data release through adding noise on average value
NSS'12 Proceedings of the 6th international conference on Network and System Security
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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.