Optimization algorithm for k-anonymization of datasets with low information loss
Optimization algorithm for k-anonymization of datasets with low information loss
复制标题
低信息损失数据集k-匿名化优化算法
DOI：
10.1007/s10207-017-0392-y
复制
发表时间：
2018
影响因子：
3.2
通讯作者：
Uno Takeaki
中科院分区：
文献类型：
--
作者：
Murakami Keisuke;Uno Takeaki
匿名化是对数据进行修改，以掩盖个人与数据中敏感信息之间的对应关系。匿名化模型如ask-anonymity已经被广泛研究。最近，提出了一种新的模型，具有更少的信息损失比现有的模型，这是一种类型的非齐次推广。在本文中，我们提出了一种替代的匿名化算法，进一步减少了使用优化技术的信息损失。我们还证明了一个修改后的数据集检查是否满足k-匿名的多项式时间算法。计算实验表明，即使在大数据集上，我们的算法的效率。
Anonymization is the modification of data to mask the correspondence between a person and sensitive information in the data. Several anonymization models such ask-anonymity have been intensively studied. Recently, a new model with less information loss than existing models was proposed; this is a type of non-homogeneous generalization. In this paper, we present an alternative anonymization algorithm that further reduces the information loss using optimization techniques. We also prove that a modified dataset is checked whether it satisfies thek-anonymity by a polynomial-time algorithm. Computational experiments were conducted and demonstrated the efficiency of our algorithm even on large datasets.