[1]刘中锋.基于局部学习的差分隐私集成特征选择算法[J].计算机技术与发展,2018,28(10):79-82.[doi:10.3969/ j. issn.1673-629X.2018.10.016]
　LIU Zhong-feng.An Ensemble Feature Selection Algorithm with Differential Privacy Based on Local Learning[J].,2018,28(10):79-82.[doi:10.3969/ j. issn.1673-629X.2018.10.016]
点击复制

基于局部学习的差分隐私集成特征选择算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年10期
页码:
79-82
栏目:
智能、算法、系统工程
出版日期:
2018-10-10

文章信息/Info

Title:
An Ensemble Feature Selection Algorithm with Differential Privacy Based on Local Learning
文章编号:
1673-629X(2018)10-0079-04
作者:
刘中锋
南京邮电大学 计算机学院、软件学院、网络空间安全学院,江苏 南京 210000
Author(s):
LIU Zhong-feng
School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210000,China
关键词:
特征选择; 集成; 差分隐私; 隐私度; 敏感度
Keywords:
feature selection; ensemble; differential privacy; privacy degree; sensitivity
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2018.10.016
文献标志码:
A
摘要:
面对海量数据,特征选择在数据挖掘和机器学习领域上通常是不可或缺的一步。 目前,机器学习安全领域受到了越来越多的关注,尤其是隐私保护方面。 然而,对于隐私保护的特征选择仍然是一个比较新的课题,特别是与集成学习相关的集成特征选择。 差分隐私是一种有着严格理论基础的隐私保护方法,因此提出了一种基于局部学习的差分隐私集成特征选择算法。 该算法的主要思想是基于一种输出干扰策略,即向输出结果添加噪声从而保护隐私,而且该噪声依赖于原始算法的隐私度和敏感度。 除了严格的理论证明之外,也从实验中展现了算法的性能。 实验采用 KNN 和 SVM 作为分类器,分别分析了隐私度和特征数量的影响。 结果显示随着隐私度的降低,提高了隐私保护程度。
Abstract:
When confronting massive data,feature selection is usually a necessary step for data mining and machine learning. Currently,secure machine learning,especially in privacy preservation,has attracted much attention. However,feature selection with privacy preservation is still a new issue,especially for feature selection related to ensemble learning. In this paper,we present a differentially private ensemble feature selection algorithm,of which the basic idea is the output perturbation where the density of perturbation noise depends on the privacy degree and sensitivity of original feature selection algorithm. Besides the theoretical proof,the experimental results also demonstrated their high performance under certain privacy preservation degree. In the experiment,KNN and SVM are selected as classifiers and the privacy degree and the number of features are researched. The results show that the privacy preserving degree is better,along with the decline of privacy degree.

相似文献/References:

[1]刘利 何先平 袁文亮.股票趋势预测中Wrapper方法的研究与应用[J].计算机技术与发展,2010,(01):209.
　LIU Li,HE Xian-ping,YUAN Wen-liang.Research and Application of Wrapper Approach to Stock Trend Prediction[J].,2010,(10):209.
[2]黄炜 黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,(06):21.
　HUANG Wei,HUANG Zhi-hua.Feature Selection Based on Genetic Algorithm and SVM[J].,2010,(10):21.
[3]张家柏 王小玲.基于聚类和二进制PSO的特征选择[J].计算机技术与发展,2010,(06):25.
　ZHANG Jia-bai,WANG Xiao-ling.A Novel Algorithm Based on K-Means Clustering and Binary Particle Swarm Optimization[J].,2010,(10):25.
[4]王志 孙涌 张书奎 王永山[].基于本体的专利数据源集成的研究及应用[J].计算机技术与发展,2009,(07):87.
　WANG Zhi,SUN Yong,ZHANG Shu-kui,et al.Research and Practice of Patent Data Source Integration Based on Ontology[J].,2009,(10):87.
[5]冯甲策 叶明 王惠文.基于Gram—Schmidt过程的支持向量机降维方法[J].计算机技术与发展,2009,(11):7.
　FENG Jia-ce,YE Ming,WANG Hui-wen.Dimension Reduction Method of Support Vector Machine Based on Gram- Schmidt Process[J].,2009,(10):7.
[6]林伟 柳荣其 徐熙.邮件过滤中一种改进的特征选择方法研究[J].计算机技术与发展,2009,(01):84.
　LIN Wei,LIU Rong-qi,XU Xi.Improvement of Feature Selection Algorithm in Spam Filtering[J].,2009,(10):84.
[7]刘毅 张月琳.基于Agent的邮件过滤与个性化分类系统设计[J].计算机技术与发展,2009,(02):66.
　LIU Yi,ZHANG Yue-lin.Design of a Mail Filter and Personalized Classification System Based on Agent[J].,2009,(10):66.
[8]陈素萍 谢丽聪.一种文本特征选择方法的研究[J].计算机技术与发展,2009,(02):112.
　CHEN Su-ping,XIE Li-cong.Research on Document Feature Selection[J].,2009,(10):112.
[9]刘为峰 徐造林 黄永葛.基于RS、GIS集成的水深探测研究[J].计算机技术与发展,2008,(02):240.
　LIU Wei-feng,XU Zao-lin,HUANG Yong-ge.Water Depth Detection Study Based on Integration of RS and GIS[J].,2008,(10):240.
[10]仇文慧 陈宇寒[].信息集成关键技术——企业服务总线[J].计算机技术与发展,2008,(09):5.
　QU Wen-hui,CHEN Yu-han.Key Technologies of Enterprise Integration： Enterprise Service Bus[J].,2008,(10):5.

更新日期/Last Update: 2018-10-10