Differential privacy is effective in sharing information and preserving privacy with a strong guarantee. As social network analysis has been extensively adopted in many applications, it opens a new arena for the application of differential privacy. In this article, we provide a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We present a concise review of the foundations of differential privacy and the major variants and discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, types of differential privacy in social network analysis, and a series of popular tasks, such as degree distribution analysis, subgraph counting and edge weights. We also discuss a series of challenges for future studies.

翻译：由于社会网络分析在许多应用中被广泛采用,因此为应用差别隐私开辟了一个新的舞台;在本条中,我们提供了一份综合调查,将差别隐私的基本原则和社会网络分析中的应用联系起来;我们简要地审查了差别隐私的基础和主要变体,并讨论了将差别隐私应用于社会网络分析的方式,包括社会网络中的隐私攻击、社会网络分析中的差别隐私类型以及一系列流行任务,如学位分配分析、子字数和边际权重。我们还讨论了未来研究的一系列挑战。