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 X...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.展开更多
基金This work is supported by the NSFC[Grant Nos.61772281,61703212,61602254]Jiangsu Province Natural Science Foundation[Grant No.BK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cation Institutions(PAPD)and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET).
文摘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.
