基于聚类的动态社交网络隐私保护方法

Clustering-based dynamic privacy preserving method for social networks

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。

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.