On Enhancing Utility in A-anonymization

摘要：

k-anonymity is one of the most well studied models of privacy preservation technology. k-anonymity protects the identification of an individuals record from at least (k-1) other records. In this paper, we propose a new quality metric, called the utility-privacy (UP) metric, which overcomes the limitations of existing one dimensional metrics, representing either privacy measure or data utility measure, used in privacy preserving data sharing. The proposed UP metric represents both the privacy measure and utility measure of the anonymous data. We then present a new heuristic algorithm that achieves k-anonymity with high utility and utility-privacy measure. Comparisons of experimental results of our algorithm with those of four other well-known algorithms for k-anonymity show that our algorithm performs the best both in utility measure and utility-privacy measure.

关键词： privacy anonymization information loss utility

作者: Md. Nurul Huda Shigeki Yamada Noboru Sonehara

作者单位: National Institute of Informatics Tokyo, Japan

会议类型: 国际会议

会议名称: 2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)

会议地点: 三亚

会议语种:英文

页码: 223-227

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）