Fuzzy Set based Data Publishing For Privacy Preservation

被引：0

作者：

Xie, Meng-bo

Qian, Quan ^{[1
]}

机构：

[1] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China

来源：

2016 17TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) | 2016年

关键词：

privacy preservation; fuzzy sets; k-anonymity;

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：

K-anonymity and its successors, like 1-diversity and t-closeness, are the most popular approaches for privacy preserving data publishing. However, each method has relatively high information loss and computational complexity. In order to solve this problem, this paper presents a fuzzy set based anonymity algorithm, where numerical data are transformed to linguistic data and sensitive data are published in conjunction with fuzzy draft rate. The experimental results show that the fuzzy based algorithm performs better than that of the k anonymity method from the points of information loss and execution performance. The information loss of the fuzzy based algorithm has been reduced by 40%similar to 50% and the execution time reduced by 48%similar to 59%.

引用

页码：569 / 574

页数：6

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