A Differential Privacy Random Forest Method of Privacy Protection in Cloud

被引:5
|
作者
Lv, Chaoxian [1 ]
Li, Qianmu [1 ]
Long, Huaqiu [2 ]
Ren, Yumei [3 ]
Ling, Fei [4 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Wuyi Univ, Intelligent Mfg Dept, Jiangmen, Peoples R China
[3] Jiangsu Womens Federation New Media & Network Inf, Nanjing, Jiangsu, Peoples R China
[4] Nanjing Liancheng Technol Dev Co Ltd, Jiangsu Postgrad Workstn, Nanjing, Jiangsu, Peoples R China
来源
2019 22ND IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (IEEE CSE 2019) AND 17TH IEEE INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (IEEE EUC 2019) | 2019年
关键词
privacy protection; differential privacy; random forest; ALGORITHM; QUERIES;
D O I
10.1109/CSE/EUC.2019.00093
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
this paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value.
引用
收藏
页码:470 / 475
页数:6
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