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This paper presents a novel privacy principle,ε-inclusion,for re-publishing sensitive dynamic datasets,ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata.Combined with generalization-based methods,ε-inclusion protects privacy and captures a large amount of correlation in the microdata.We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement.Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods. 相似文献
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Qiong WEI Yan-sheng LU Lei ZOU 《浙江大学学报(A卷英文版)》2008,9(8):1124-1133
This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets, ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ε-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods. 相似文献
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