Privacy-Preserving Gradient Boosting Decision Trees

@inproceedings{Li2020PrivacyPreservingGB,
  title={Privacy-Preserving Gradient Boosting Decision Trees},
  author={Q. Li and Zhaomin Wu and Zeyi Wen and Bingsheng He},
  booktitle={AAAI},
  year={2020}
}
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective… 
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