摘要
K-匿名是数据发布应用场景下重要的隐私保护模型。近年来数据集K-匿名化的算法得到广泛的研究,Median Mondrian算法是目前唯一的多维K-匿名划分方法。文中研究了Median Mondrian算法,指出其不能有效地平衡数据划分精度与数据隐私安全性之间的矛盾,由此提出基于熵测度机制的多维K-匿名划分方法以及评估K-匿名化结果安全性的测量标准。实验表明该算法是可行的,能有效地提高数据安全性。
K-anonymity is an important privacy preserving model in the data publishing scenario. The algorithms on dataset K-anonymization are researched extensively in recent years, Median Mondrian algorithm is the only multidimensional K-anonymity partition method. However, our research shows that Median Mondrian algorithm is not well-balanced on dealing with the contradiction between data partition precision and data privacy preserving. In this paper, we propose an entropy-based multidimensional K-anonymity partition method and a new evaluation measure on K-anonymization results. The experimental results show that our new method is feasible and preserves the privacy much more efficiently than Median Mondrian algorithm.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2007年第6期1228-1231,共4页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(60471055)
关键词
熵
K-匿名
多维划分
准标识符
entropy
K-anonymity
multidimensional partition
quasi-identifier
