Yuanmin Shi1,2, Siran Yin1,2, Ze Chen1,2, Leiming Yan1,2,*
Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193
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. More >
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