Applications of Differential Privacy in Social Network Analysis: A Survey

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 1; pp. 108 - 127
• Main Authors: Jiang, Honglu, Pei, Jian, Yu, Dongxiao, Yu, Jiguo, Gong, Bei, Cheng, Xiuzhen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; degree distributions; dependent differential privacy; Differential privacy; edge weight query; global sensitivity; Graph theory; local differential privacy; Network analysis; Privacy; Publishing; Sensitivity; smooth sensitivity; social network data analysis; Social networking (online); Social networks; subgraph counting
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2021.3073062

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.