A Personalized (α, k)-Anonymity Model

摘要：

One important privacy principle is that an individual has the freedom to decide his/her own privacy preferences, which should be taken into account when data holders release their privacy preserving microdata. Nevertheless, current related k-anonymitymodel research focuses on protecting individual private information by using pre-defined constraint parameters specified by data holders. This paper introduces a personalized (α, k) model by introducing a vector for describing individual personalized privacy requirements corresponding to each value in the domain of sensitive attributes by data respondents, and propose an ef-ficiency anonymization algorithm which combines the topdown specialization for quasi-identifier anonymization and the local recoding technique for the sensitive attribute generalization based on its attribute taxonomy tree. Experimental results show that this approach can meet better personalized privacy requirements and keep the information loss low.

作者: Xiaojun Ye Yawei Zhang Ming Liu

作者单位: Key Laboratory for Information System Security,Ministry of Education School of Software,Tsinghua University,100084 Beijing,China

会议类型: 国际会议

会议名称: The Ninth International Conference on Web-Age Information Management(第九届web时代信息管理国际会议)(WAIM 2008)

会议地点: 张家界

会议语种:英文

在线出版日期: 2008-07-20（万方平台首次上网日期，不代表论文的发表时间）