基于敏感性分级的(αi,k)-匿名隐私保护

摘要

(a,k)-匿名模型未考虑敏感属性不同取值间的敏感性差异,不能很好地抵御同质性攻击.同时传统基于泛化的实现方法存在效率低、信息损失量大等缺点.为此,提出一种基于敏感性分级的(αi,k)-匿名模型,考虑敏感值之间的敏感性差异,引入有损连接思想,设计基于贪心策略的(αi,k)-匿名聚类算法.实验结果表明,该模型能抵御同质性攻击,是一种有效的隐私保护方法.%(α,k)-anonymity model can not thwart the homogeneity attack well because of the model ignoring the sensitive difference between sensitive attribute.It is achieved traditionally via generalization techniques.It also has some defects on efficiency and data distortion.So this paper proposes an improved (αi, k)-anonymity model.It considers the sensitive difference between sensitive attribute, and designs a (αi, k)-anonymity clustering algorithm based on greedy strategy recur to the idea of lossy join.Experimental results show that the proposed model can resist homogeneity attack and is an effective approach.