面向敏感值的个性化隐私保护

Individuation Privacy Preservation Oriented to Sensitive Values

现有隐私保护匿名模型不能实现敏感值的个性化保护,为此,论文提出完全(α,k)-匿名模型,该模型通过设置等价类中敏感值的出现频率来实现敏感值的个性化保护.论文还提出(,αk)-聚类算法来实现各种(α,k)-匿名模型.实验表明:完全(,αk)-匿名模型能够以与其它(,αk)-匿名模型近似的信息损失量和时间代价,获得更好的隐私保护.

Existing anonymity models for privacy preservation cannot implement individuation preservation oriented to sensitive values.To solve the problem,the paper proposes a complete （α,k）-anonymity model which can implement individuation privacy preservation for sensitive values by setting the frequency constraints on each sensitive value in every equivalence class.The paper also proposes a （α,k）-clustering algorithm to implement all kinds of （α,k）-anonymity models.Experimental results show that the complete （α,k）-anonymity model provides better privacy preservation than other （α,k）-anonymity models with the similar information loss and execution time.