文章摘要
动态社交网络中基于结构多样性的 隐私保护方案
  
DOI:
中文关键词: 社交网络  隐私保护  敌人  结构多样性  匿名化
英文关键词: 
基金项目:院级科研基金项目(2014011)
作者单位
孙莉娜 辽宁机电职业技术学院 信息工程系辽宁 丹东 118009 
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中文摘要:
      当前许多隐私保护技术主要针对静态社交网络.然而,由于数据连续发布,动态社交网络也存在隐私泄露问题.为了防范敌人的攻击,引入一种新的动态隐私保护方法,称为动态kw重结构多样性匿名法kw-SDA.该方法通过对个体分组保护,将连续发布数据时结点/社区身份的泄露概率限制为1/k.然后,提出一种可以实现动态kw-SDA算法的可拓展启发式算法.该算法可根据前w-1次发布的数据对图形进行匿名化处理,使图形改动最小化.此外,通过引入CS表,该算法可以逐渐汇总连续数据发布时的结点信息,避免了匿名化处理时扫描发布的所有数据.评估结果表明,该方法既能保护网络的大部分特征,又能有效保护隐私.
英文摘要:
      Nowadays most prior privacy protection techniques focus on static social networks. However, there are additional privacy disclosures in dynamic social networks due to the sequential publications. To protect against such an adversary, a new dynamic privacy scheme was introduced, named kw-SDA(dynamic kwstructural diversity anonymity).Which ensured that the probability of a vertex/ community identity being revealed in sequential releases was limited to 1/k by protecting individuals in groups. After that, a scalable heuristic algorithm was develaped to provide dynamic kw-SDA. The proposed algorithm anonymize the graph based on the previous w-1 releases and minimize the graph alterations. Moreover, by introducing a table, named CS-Table, the algorithm incrementally summarized the vertex information in sequential releases, and avoided the need of scanning all the releases for anonymization. The evaluations show that the approach retain much of the characteristics of the networks while confirming the privacy protection.
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