Applications of Differential Privacy in Social Network Analysis: A Survey

IEEE Transactions on Knowledge and Data Engineering(2023)

引用 65|浏览111
暂无评分
摘要
Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.
更多
查看译文
关键词
Differential privacy,social network data analysis,global sensitivity,smooth sensitivity,local differential privacy,dependent differential privacy,degree distributions,subgraph counting,edge weight query
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要