摘要
当前网络公开数据中的隐私泄露问题频出,给相关个人造成不良影响甚至严重危害,隐私保护技术研究因此越来越受到关注。k-匿名化作为一种能够有效保护隐私信息的技术,已发展了多种算法,但这些算法有的数据处理效率较低、有的抗攻击性能较弱。文章采用K-means算法并结合运用Mondrian算法进行聚类处理,建立了一种基于K-means的(k,e)匿名隐私保护的改进算法。不仅与具有代表性的隐私保护算法(k,e)-MDAV算法进行了运算效率的对比,还利用改进算法进行了涉及个人位置信息的应用案例分析。结果表明,文章提出的改进算法在实现数据匿名化基础上,能有效提高运行效率,且具有较强的抗链接攻击和抗同质化攻击性能。
At present,the problem of privacy disclosure in public data of the network frequently appears,which has caused adverse effects and even serious harm to the relevant individuals.Therefore,the research on privacy protection technology has attracted more and more global attention.As a technology that can effectively protect privacy information,k-anonymization has developed a variety of algorithms,however,some of these algorithms have low data processing efficiency and weak anti-attack performance.This paper established an improved algorithm of(k,e)anonymous privacy protection based on K-means by using the K-means algorithm and the Mondrian algorithm for clustering;and it not only compared the computational efficiency with the representative privacy protection algorithm(k,e)-MDAV algorithm,but also used the improved algorithm to analyze an application case involving personal location information.The results show that the improved algorithm proposed in this paper can effectively improve the operation efficiency based on the implementation of anonymous data,and has strong anti-link attack and anti-homogeneity attack performance.
作者
顾海艳
蒋铜
马卓
朱季鹏
GU Haiyan;JIANG Tong;MA Zhuo;ZHU Jipeng(Department of Computer Information and Cyber Security,Jiangsu Police Institute,Nanjing 210031,China;Xuzhou Municipal Public Security Bureau,Xuzhou 221000,China;Nantong Municipal Public Security Bureau,Nantong 226000,China)
出处
《信息网络安全》
CSCD
北大核心
2022年第10期52-58,共7页
Netinfo Security
基金
国家自然科学基金[62202209]。
关键词
K-匿名
聚类算法
改进算法
隐私保护
k-anonymity
clustering algorithm
improved algorithm
privacy protection
