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(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack

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发布时间:2018-12-18

第一作者:Haiyuan Wang

发表时间:2014-01-01

发表刊物:Journal of Computers

所属单位:数理与信息工程学院

文献类型:期刊

卷号:Vol.9

期号:No.1

页面范围: 59-64

ISSN号:1796-203X

关键字:Data;privacy;l-diversity;(l;e)-diversity;anatomy;semantic;similarity;attack

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 s

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