【摘要】 已有的k-匿名方法忽视了准标识符对不同敏感属性的影响且只考虑了对元组本身的聚类,在数据发布时造成了较大的信息损失。为此,提出一种通过两次聚类实现k-匿名的隐私保护方法。给出了影响矩阵的概念,用来描述准标识符对敏感属性的影响,研究了影响矩阵聚类技术,对敏感属性影响相近的元组进行聚类,实现k-匿名效果。实验验证结果表明,该方法具有良好的隐私保护效果,相对于基本k-匿名方法,该方法具有更小的平均等价类大小和更少的运行时间。更多还原

【Abstract】 k-anonymity is a current hot spot for privacy preservation.The existing k-anonymous methods ignored the quasi-identifier’s different influences on the sensitive attributes and clustered the tuples only,which caused much information loss while publishing the data.To cope with this problem,a novel k-anonymity via twice clustering and the concept of influence matrix to express the quasi-identifier’s influences on different sensitive attributes are proposed.The clustering techniques over influence matrix are investigated and the tuples with near influences on the sensitive attributes are clustered to achieve k-anonymity.The experimental results show that the proposed methods are effective and feasible to privacy preservation.Compared with basic k-anonymity,the methods have less average equivalence class size and less run time.更多还原