Applications of Differential Privacy in Social Network Analysis: A Survey
arXiv:2010.02973
Abstract
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.
arXiv comment50 pages,16 figures, 5 tables
References in corpus (6)
- De-anonymizing Social Networks
- Differential Privacy: An Economic Method for Choosing Epsilon
- Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions
- Privacy and National Security Issues in Social Networks: The Challenges
- Differentially Private Continual Release of Graph Statistics
- Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models