Utility-Based Anonymization for Multiple Sensitive Attributes
Privacy-preserving data publication problem has attracted more and more attention in recent years.A lot of related research works have been done towards dataset with single sensitive attribute.However,usually,original data contains more than one sensitive attribute.In this paper,we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes.We first cluster the sensitive attribute values based on a utility matrix.We calculate the utility for both numeric attribute and categorical attribute respectively.Then we use a greedy strategy to partition the tuples into equivalence classes.Our method can guarantee that the size of the equivalence class is k except the last one,which reduces the information loss.And we can guarantee the diversity of the sensitive value in an equivalence class,which can protect the privacy against the homogeneity attack and similarity attack.Experiments on real dataset show that our method performs well on information loss,which indicates that we can guarantee the data utility while protecting the personal privacy.
国内会议
湖北恩施
英文
1-7
2014-09-13（万方平台首次上网日期，不代表论文的发表时间）