Quantitative association rules mining methods with privacy-preserving

被引：0
|
作者：
Chen, ZY [1 ]
Liu, GH [1 ]
机构：
[1] Yanshan Univ, Dept Comp Sci & Engn, Qinhuangdao 066004, Peoples R China
来源：
PDCAT 2005: Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Proceedings | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Considering the different size of quantitative attribute values and categorical attribute values in databases, we present two quantitative association rules mining methods with privacy-preserving respectively, one bases on Boolean association rules, which is suitable for the smaller size of quantitative attribute values and categorical attribute values in databases,. the other one bases on partially transforming measures, which is suitable for the larger ones. To each approach, the privacy and accuracy are analyzed, and the correctness and feasibility are proven by experiments.
引用
收藏
页码：910 / 912
页数：3
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