基于敏感度的个性化(α,l)-匿名方法

Personalized(α,l)-anonymity Method Based on Sensitivity

目前多数隐私保护匿名模型不能满足面向敏感属性值的个性化保护需求,也未考虑敏感属性值的分布情况,易受相似性攻击。为此,提出基于敏感度的个性化(α,l)-匿名模型,通过为敏感属性值设置敏感度,并定义等敏感度组的概念,对等价类中各等敏感度组设置不同的出现频率,满足匿名隐私保护的个性化需求。通过限制等价类中同一敏感度的敏感属性值出现的总频率,控制敏感属性值的分布,防止相似性攻击。提出一种基于聚类的个性化(α,l)-匿名算法,实现匿名化处理。实验结果表明,该算法能以与其他l-多样性匿名模型近似的信息损失量和时间代价,提供更好的隐私保护。

Currently,the majority of anonymity model for privacy preservation neither meets the need of personalized preservation oriented to sensitive attribute values,nor considers the distribution of sensitive attribute values,therefore they are vulnerable to similarity attack. This paper proposes a personalized（α,l）-anonymity model based on sensitivity. This model provides sensitivity for different sensitive attribute values,and defines the concept of equ-sensitivity group,implements the personalized needs of privacy anonymity by setting the frequency constraints for different equ-sensitivity group in every equivalence class. This model also can defense similarity attack by limiting the total frequency of sensitive attribute values for same sensitivity in every equivalence class,and control the distribution of sensitive attribute values.This paper proposes a personalized（α,l）-anonymity algorithm based on clustering to achieve the purpose of anonymity.Experimental results show that the proposed algorithm provides better privacy preservation than other l-diversity anonymity models with the similar information loss and time cost.