Open Access

ARTICLE

XGBoost Algorithm under Differential Privacy Protection

Yuanmin Shi^1,2, Siran Yin^1,2, Ze Chen^1,2, Leiming Yan^1,2,*

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. https://doi.org/10.32604/jihpp.2021.012193

Received 30 August 2020; Accepted 12 December 2020; Issue published 21 April 2021

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.

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Keywords

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