摘要
为了防止链接攻击导致隐私的泄露,同时尽可能降低匿名保护时的信息损失,提出(λα,k)-分级匿名模型。该模型根据隐私保护的需求程度,将各敏感属性值划分为高、中、低三个等级类,通过隐私保护度参数λ灵活控制泄露风险。在此基础上,给出一种基于聚类的分级匿名方法。该方法采用一种新层次聚类算法,并针对准标识符中数值型属性与分类型属性采用灵活的概化策略。实验结果显示,该方法能够满足敏感属性的分级匿名保护需求,同时有效地减少信息损失。
To prevent the privacy disclosure caused by linking attack and reduce information loss resulting from anonymous protection, a （λα, k） multi-level anonymity model was proposed. According to the requirement of privacy preservation, sensitive attribute values could be divided into three levels： high, medium, and low. The risk of privacy disclosure was flexibly controlled by privacy protection degree parameter λ. On the basis of this, clustering-based approach for multi-level anonymization was proposed. The approach used a new hierarchical clustering algorithm and adopted more flexible strategies of data generalization for numerical attributes and classified attributes in a quasi-identifier. The experimental results show that the approach can meet the requirement of multi-level anonymous protection of sensitive attribute, and effectively reduce information loss.
出处
《计算机应用》
CSCD
北大核心
2013年第2期412-416,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61262075)
广西高等学校重大科研项目(201201ZD012)
广西教育厅科研项目(200911LX119)
关键词
隐私保护
数据发布
数据匿名
分级
聚类
信息损失
privacy preservation
data publishing
data anonymization
multi-level
clustering
information loss
