Clustering-based dynamic privacy preserving method for social networks

被引：0
作者：
Beijing Key Laboratory of Intelligent Telecommunications software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing [1 ]
100876, China
不详 [2 ]
201204, China
不详 [3 ]
100876, China
机构：
[1] Beijing Key Laboratory of Intelligent Telecommunications software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
[2] The Third Research Institute of Ministry of Public Security, Shanghai
[3] School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing
来源：
Tongxin Xuebao |
基金：
中国国家自然科学基金;
关键词：
Anonymization rate; Clustering; Dynamic social networks; Information loss degree; Privacy preserving;
D O I：
10.11959/j.issn.1000-436x.2015290
中图分类号：
学科分类号：
摘要：
Due to the dynamic characteristics of the social network graph structure, an effective dynamic privacy preserving method is needed. To solve the problems of the existing dynamic privacy preservation methods, such as attacker's too little background knowledge and the low adaptability to the dynamic characteristics of graph structure, a clustering-based dynamic privacy preservation method is provided. The analysis shows that the proposed method can resist many kinds of background knowledge attacks and has good adaptability to the dynamic characteristics of the social network graph structure. © 2015, Editorial Board of Journal on Communications. All right reserved.
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页数：5
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