An effective scheme for top-k frequent itemset mining under differential privacy conditions

被引：0

作者：

Wenjuan LIANG ^{[1
,2
]}

Hong CHEN ^{[1
]}

Jing ZHANG ^{[1
]}

Dan ZHAO ^{[1
]}

Cuiping LI ^{[1
]}

机构：

[1] Key Lab of Data Engineering and Knowledge Engineering of MOE,Renmin University of China

[2] College of Computer and Information Engineering,Henan University

来源：

Science China(Information Sciences) | 2020年 / 63卷 / 05期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

An effective scheme for top-k frequent itemset mining under differential privacy conditions;

D O I：

暂无

中图分类号：

TP311.13 []; TP309 [安全保密];

学科分类号：

081201 ; 0839 ; 1201 ; 1402 ;

摘要：

Dear editor,Frequent itemset mining (FIM) is important in many data mining applications [1], such as web log mining and trend analysis. However, if the data are sensitive (e.g., web browsing history), directly releasing frequent itemsets and their support may breach user privacy. The protection of user privacy while obtaining statistical information is im-

引用

页码：200 / 202

页数：3