中文摘要：

社会网络在最近的年里正在得到越来越多的注意。人们加入社会网络与其它一起分享他们的信息。由于不同文化和背景，然而，人们有什么样的信息应该被出版上的不同要求。当前，当社会网络网站出版数据时，他们就留下一个用户感到敏感空白的信息。这不由于标签结构关系的存在是足够。一组分析算法能被用来与高精确性学习空白的信息。在这份报纸，我们建议一个个性化的模型在社会网络保护私人信息。明确地，我们由稍微在一些用户邻居改变边打破标签结构协会。更重要地，以便增加出版的图的可用性，我们也在隐私保护期间保存每个用户的影响价值。我们通过广泛的实验验证我们的方法的有效性。结果证明建议方法能保护敏感标签免于学习算法并且同时，保存某些图实用程序。

英文摘要：

Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high accuracy. In this paper, we propose a personalized model to protect private information in social networks. Specifically, we break the label-structure association by slightly changing the edges in some users＇ neighborhoods. More importantly, in order to increase the usability of the published graph, we also preserve the influence value of each user during the privacy protection. We verify the effectiveness of our methods through extensive experiments. The results show that the proposed methods can protect sensitive labels against learning algorithms and at the same time, preserve certain graph utilities.