@Article{jihpp.2021.012193,
AUTHOR = {Yuanmin Shi, Siran Yin, Ze Chen, Leiming Yan},
TITLE = {XGBoost Algorithm under Differential Privacy Protection},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {9--16},
URL = {http://www.techscience.com/jihpp/v3n1/42326},
ISSN = {2637-4226},
ABSTRACT = {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.},
DOI = {10.32604/jihpp.2021.012193}
}