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XGBoost Algorithm under Differential Privacy Protection

Yuanmin Shi1,2, Siran Yin1,2, Ze Chen1,2, Leiming Yan1,2,*

1 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, China

* Corresponding Author: Leiming Yan. Email:

Journal of Information Hiding and Privacy Protection 2021, 3(1), 9-16.


Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.


Cite This Article

Y. Shi, S. Yin, Z. Chen and L. Yan, "Xgboost algorithm under differential privacy protection," Journal of Information Hiding and Privacy Protection, vol. 3, no.1, pp. 9–16, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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