[1]万文强,张伶卫.分布式环境下的隐私保护特征选择研究[J].南京师范大学学报(工程技术版),2012,12(03):060-67.
　Wan Wenqiang,Zhang Lingwei.Privacy Preserving Feature Selection in Distributed Environment[J].Journal of Nanjing Normal University(Engineering and Technology),2012,12(03):060-67.
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分布式环境下的隐私保护特征选择研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
12卷
期数:
2012年03期
页码:
060-67
栏目:
出版日期:
2012-09-20

文章信息/Info

Title:
Privacy Preserving Feature Selection in Distributed Environment
作者:
万文强;张伶卫;
南京邮电大学计算机学院，江苏南京210003
Author(s):
Wan Wenqiang; Zhang Lingwei
College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
隐私保护; 特征选择; 分布式; 微分隐私; 主成分分析
Keywords:
privacy preserving; feature selection; distribution; differential privacy; principal component analysis
分类号:
TP309
摘要:
在Map-Reduce的分布式环境框架下,基于微分隐私与主成分分析,并与熵、误分类增益、基尼指数等统计量相结合,提出了一种新的在分布式环境下的隐私保护特征选择算法,实现了在保护数据集隐私的同时保护特征的隐私.仿真实验结果表明,该算法具有较好的性能,能够在保护一定程度隐私信息的同时,有效地进行特征选择.
Abstract:
Privacy preserving and feature selection are very important in data mining． Thus，how to select feature effectively based on privacy preserving is also a hot topic． Under the Map-Reduce distributed environment framework，proposed was the combination of the differential privacy and principal component analysis with the statistics including entropy， misclassification gain，and gini index，a new privacy preserving feature selection algorithm on distributed environment． The algorithm achieved the purposes of protecting privacy of both data sets and features． The simulation results on several bench-mark data sets indicated that this algorithm performed well． During the selection of the important features， it could protect privacy information to a certain extent．

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 61073114) ．通讯联系人: 万文强，硕士，研究方向: 数据挖掘与机器学习． E-mail: aiaiyouchou@163． com
更新日期/Last Update: 2013-03-11