电子科技大学学报2017，Vol.46Issue(6)：896-901,6.DOI:10.3969/j.issn.1001-0548.2017.06.018
多属性泛化的K-匿名算法
K-Anonymity Algorithm Based on Multi Attribute Generalization
摘要
Abstract
Aiming at the major issues for data over-generalization and no unique attributes of K-anonymity model, a modified K-anonymity algorithm based on multiple attributes generalization is proposed in this paper. The conception of attribute approximation degree is introduced which describes the discrete degree of quasi-identifiers, and determines the candidate quasi-identifier attribute to be generalized. In the meantime, breadth-first generalization is exploited to avoid over-generalization and meets the K-anonymity requirements ultimately. The experimental results show that the new K-anonymity algorithm based on multiple attribute generalization can improve data precision and its efficiency is equal to Datafly algorithm. The proposed algorithm can effectively solve the issue of generalization attribute selecting when quasi-identifiers are not unique, the over-generalization of quasi-identifiers attributes can be avoided, and the usability of data can be improved.关键词
泛化/K-匿名/隐私保护/关系型数据Key words
generalization/K-anonymity/privacy protecting/relational data分类
信息技术与安全科学引用本文复制引用
宋明秋,王琳,姜宝彦,邓贵仕..多属性泛化的K-匿名算法[J].电子科技大学学报,2017,46(6):896-901,6.基金项目
国家自然科学基金面上项目(71171028) （71171028）
国家科技支撑计划(2013BAH01B03) （2013BAH01B03）
