DiffPRFs: Random forest under differential privacy

被引：0
作者：
Mu H.-R. [1 ]
Ding L.-P. [1 ]
Song Y.-N. [1 ]
Lu G.-Q. [1 ]
机构：
[1] National Engineering Research Center of Fundamental Software, Institute of Software, Chinese Academy of Sciences, Beijing
来源：
| 1600年 / Editorial Board of Journal on Communications卷 / 37期
关键词：
Data mining; Differential privacy; Privacy protection; Random forest;
D O I：
10.11959/j.issn.1000-436x.2016169
中图分类号：
学科分类号：
摘要：
A differential privacy algorithm DiffPRFs based on random forests was proposed. Exponential mechanism was used to select split point and split attribute in each decision tree building process, and noise was added according to Laplace mechanism. Differential privacy protection requirement was satisfied through overall process. Compared to existed algorithms, the proposed method does not require pre-discretization of continuous attributes which significantly reduces the performance cost of preprocessing in large multi-dimensional dataset. Classification is achieved conveniently and efficiently while maintains the high accuracy. Experimental results demonstrate the effectiveness and superiority of the algorithm compared to other classification algorithms. © 2016, Editorial Board of Journal on Communications. All right reserved.
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页码：175 / 182
页数：7
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