社会网络子集(θ,k)-匿名方法  

(θ,k)-anonymous method in the subsets of social networks

作　　者：张晓琳[1] 王萍[1] 郭彦磊[1] 王静宇[1] 

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《计算机应用》2015年第8期2178-2183,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61163015)

摘　　要：针对目前社会网络邻域隐私保护相关研究并没有考虑对子集的保护,并且邻域子集中的特定属性分布情况也会造成个体隐私泄露这一问题,提出了一种新的(θ,k)-匿名模型。该模型移除社会网络中需要被保护的节点邻域子集标签后,基于k-同构思想,利用邻域组件编码技术和节点精炼方法处理候选集中的节点及其邻域子集信息,完成同构操作,其中考虑特定敏感属性分布问题。最终,该模型满足邻域子集中的每个节点都存在至少k-1个节点与其邻域同构,同时要求每个节点的属性分布在邻域子集内和在整个子集的差值不大于θ。实验结果表明,(θ,k)-匿名模型能够降低匿名成本并且最大化数据效用。Focusing on the issue that the current related research about social network do not consider subsets for neighborhood＇s privacy preserving, and the specific properties of neighborhood subsets also lead individual privacy disclosure,a new（ θ, k）-anonymous model was proposed. According to the k-isomorphism ideology, the model removed labels of neighborhood subsets which needed to be protected in social network, made use of neighborhood component coding technique and the method of node refining to process nodes in candidate set and their neighborhood information, then completed the operation of specific subsets isomorphism with considering the sensitive attribute distribution. Ultimately, the model satisfies that each node in neighborhood subset meets neighborhood isomorphism with at least k- 1 nodes, as well the model requires the difference between the attribute distribution of each node in the neighborhood subset and the throughout subsets is not bigger than θ. The experimental results show that,（ θ, k）-anonymous model can reduce the anonymization cost and maximize the utility of the data.

关 键 词：社会网络 邻域子集 属性分布 k-同构 (θ k)-匿名模型 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]