面向缺损数据的(α,k)-匿名模型

(α,k)-anonymized Model for Missing Data

在数据集对外发布之前,需要对数据集的准标识符属性进行匿名,以防遭受链接攻击。然而现有的数据匿名算法都是面向完整数据进行,对于数据集中含有缺损数据的元组会进行直接删除操作,降低了数据的可用性。文中提出将缺损数据与完整数据混合匿名的算法,并且结合了(α,k)-匿名算法。实验得出的数据充分证明:改进后的面向缺损数据的(α,k)-匿名模型有效提升了匿名后数据的可用性,实现了数据匿名。

Before a dataset is published,the quasi-identifier attributes of the dataset need to be anonymous in case of a link attack.However,the existing data anonymity algorithms are all oriented to complete data,and the tuples containing defective data in the data set will be deleted directly,which reduces the availability of data.In this paper,the missing data and intact data are mixed into an anonymous algorithm,and the(α,k)-anonymous algorithm is combined.The experiment data fully prove that the improved defective data oriented(α,k)-anonymous model effectively improves the availability of the anonymous data and realizes the data anonymity.