文章基本信息

标题：(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack
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作者：Wang, Haiyuan ; Han, Jianmin ; Wang, Jiyi 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：1
页码：59-64
DOI：10.4304/jcp.9.1.59-64
语种：English
出版社：Academy Publisher
摘要：Existing sensitive attributes diversity models do not capture the semantic similarity between sensitive values, so they cannot resist semantic similarity attack. To address the problem, we present a method to measure semantic similarity of a categorical sensitive attribute based on the attribute’ semantic hierarchy tree. On basis of the measurement, the paper proposes a ( l , e )-diversity model which has two constraints in each equivalence class: (1) there are at least l well-represented values; (2) any two sensitive values are not e -similar. Furthermore, the paper designs a liner-complexity maximum bucketization greedy algorithm to implement the model. Experimental results show that the anonymous data satisfied ( l , e )-diversity has a higher diversity degree than that satisfied l -diversity, so ( l , e )-diversity can protect privacy more effectively than l -diversity.
关键词：Data privacy;l-diversity;(l;e)-diversity;anatomy;semantic similarity attack