计算机科学与探索2012，Vol.6Issue(11)：961-973,13.DOI:10.3778/j.issn.1673-9418.2012.11.001
大数据模式分解的隐私保护研究
Privacy Preserving Based on Model Division for Large Data
摘要
Abstract
Most of the existing privacy preserving techniques often ignore special relation between sensitive attribute values and quasi-identifier attributes. At the same time, data privacy preserving need make anonymous publishing to meet composite privacy constraint for various field requirements. This paper proposes an efficient cluster algorithm based on model division for large data privacy preserving, by analyzing composite privacy constraint and similar sensitive attribute values. Firstly, it presents the clustering of sensitive attribute values to protect similar ones, and sets different weight to retain important quasi-identifier attributes. Secondly, the utility matrix of three-dimensional irregular matrix is used to obtain anonymous data with high accuracy and achieve the mode decomposition of anonymous data. Finally, experimental results on real data sets show that the data accurate rate and data error correction rate of the proposed algorithm obviously increase, and the approximate attack rate decreases.关键词
数据隐私保护/属性聚类/模式分解Key words
data privacy preserving/ attributes clustering/ model division分类
信息技术与安全科学引用本文复制引用
李宁,朱青..大数据模式分解的隐私保护研究[J].计算机科学与探索,2012,6(11):961-973,13.基金项目
The National Natural Science Foundation of China under Grant No.61070053(国家自然科学基金). （国家自然科学基金）
