通信学报 ›› 2015, Vol. 36 ›› Issue (Z1): 126-130.doi: 10.11959/j.issn.1000-436x.2015290

• 学术论文 • 上一篇    下一篇

基于聚类的动态社交网络隐私保护方法

谷勇浩1,林九川2,郭达3   

  1. 1 北京邮电大学 计算机学院 智能通信软件与多媒体北京市重点实验室,北京 100876
    2 公安部第三研究所,上海 201204
    3 北京邮电大学 电子工程学院,北京 100876
  • 出版日期:2015-11-25 发布日期:2015-12-29
  • 基金资助:
    国家自然科学基金资助项目;工业和信息化部通信软科学基金资助项目;信息网络安全公安部重点实验室开放课题基金资助项目

Clustering-based dynamic privacy preserving method for social networks

Yong-hao GU1,Jiu-chuan LIN2,Da GUO3   

  1. 1 Beijing Key Laboratory of Intelligent Telecommunications software and Multimedia,School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 The Third Research Institute of Ministry of Public Security,Shanghai 201204,China
    3 School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Online:2015-11-25 Published:2015-12-29
  • Supported by:
    The National Natural Science Foundation of China;Communication Soft Science Foundation of Ministry of Industry and Information

摘要:

由于社交网络图结构的动态变化特性,需要采用有效的动态隐私保护方法。针对现有动态数据发布隐私保护方法中存在的攻击者背景知识单一、对图结构动态变化适应性较低等问题,提出基于聚类的动态图发布隐私保护方法。分析表明,该方法能抵御多种背景知识攻击,同时对社交网络图结构动态变化具有较好的适应性。

关键词: 动态社交网络, 隐私保护, 聚类, 信息损失度, 隐匿率

Abstract:

Due to the dynamic characteristics of the social network graph structure,an effective dynamic privacy preserving method was 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 was 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.

Key words: dynamic social networks, privacy preserving, clustering, information loss degree, anonymization rate

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