采用熵的多维K-匿名划分方法  

Multidimensional K-anonymity Partition Method Using Entropy

作　　者：晏华[1] 刘贵松[1] 

机构地区：[1]电子科技大学计算智能实验室,成都610054

出　　处：《电子科技大学学报》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)

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.

关 键 词：熵 K-匿名 多维划分 准标识符 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]