多维数值型敏感属性数据的个性化隐私保护方法

为了解决多维数值型敏感属性数据隐私保护方法中存在的准标识符属性信息损失大，以及不能满足用户对数值型敏感属性重要性排序的个性化需求问题，提出一种基于聚类和加权多维桶分组（MSB）的个性化隐私保护方法。首先，根据准标识符的相似程度，将数据集划分成若干准标识符属性值相近的子集；然后，考虑到用户对敏感属性的敏感程度不同，将敏感程度和多维桶的桶容量用于计算加权选择度和构建加权多维桶；最后，依此对数据进行分组和匿名化处理。选用UCI的标准Adult数据集中的8个属性进行实验，并与基于聚类和多维桶的数据隐私保护方法MNSACM和基于聚类和加权多维桶分组的个性化隐私保护方法WMNSAPM进行对比。实验结果表明，所提方法整体较优，并且在减少信息损失和运行时间方面明显优于对比方法，提高了数据质量和运行效率。

Personalized privacy protection method for data with multiple numerical sensitive attributes

The existing privacy protection methods for data with multiple numerical sensitive attributes not only have the problem of large loss of information about quasi-identifier attributes， but also have the problem that they cannot satisfy the user’s personalized need for ranking the importance of numerically sensitive attributes. To solve the above problems， a personalized privacy protection method based on clustering and weighted Multi-Sensitive Bucketization （MSB） was proposed. Firstly， according to the similarity of quasi-identifiers， the dataset was divided into several subsets with similar values of quasi-identifier attributes. Then， considering the different sensitivities of users to sensitive attributes， the sensitivity and the bucket capacity of multi-dimensional buckets were used to calculate the weighted selectivity and to construct the weighted multi-dimensional buckets. Finally， the data were grouped and anonymized according to all above. Eight attributes in UCI’s standard Adult dataset were selected for experiments， and the proposed method was compared with MNSACM and WMNSAPM. Experimental results show that the proposed method is better generally and is significantly superior to the comparison methods in reducing information loss and running time， which improves the data quality and operating efficiency.