Development of Social Network Analysis Which Considers Privacy Preservation

为了将社交网络数据发布给众多数据分析人员，我们应该在社交网络服务中应用匿名化来保护用户的隐私。研究了一种实用安全的社交网络数据匿名化算法。重点研究了k度和k自同构两种匿名化方法，提出了处理具有节点属性的社会网络图的算法和算法。此外，我们还提出了在匿名化后能保持原始SN数据特征的算法。

In order to publish the social network data to provide the data to many data analysts, we should apply anonymization for protecting users' privacy in the social network service. We investigate the useful and secure anonymization algorithm for social network data. Especially we focus on two anonymization measure named k-degree and k-automorphism, and we propose the algorithms which can deal with the social network graph with node attributes and the algorithms. In addition we propose the algorithms which can keep the feature of original SN data after anonymization.