%0 Journal Article %A Meishu ZHANG %A Yabin XU %T Personalized privacy protection method for data with multiple numerical sensitive attributes %D 2020 %R 10.11772/j.issn.1001-9081.2019091639 %J Journal of Computer Applications %P 491-496 %V 40 %N 2 %X

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.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019091639