  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.
