采用熵的多维K-匿名划分方法

Multidimensional K-anonymity Partition Method Using Entropy

  • 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。

     

    Abstract: 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.

     

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