Privacy information propagation in online social networks - a case study based on Weibo data

• Published in: International journal of information security Vol. 24; no. 1; p. 32
• Main Authors: Luo, Yehong, Zhu, Nafei, Wang, Ziwen, Sun, Lei, He, Jingsha, Jurcut, Anca Delia, Yi, Yuzi, Ma, Xiangjun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025; Springer Nature B.V
• Subjects: Coding and Information Theory; Communications Engineering; Computer Communication Networks; Computer Science; Cryptology; Cybersecurity; Information dissemination; Management of Computing and Information Systems; Networks; Operating Systems; Policies; Privacy; Propagation; Regular Contribution; Sensitivity analysis; Social networks; Privacy information propagation; Weibo; Propagation path reconstruction; Privacy information
• ISSN: 1615-5262; 1615-5270
• DOI: 10.1007/s10207-024-00946-5

The ever-increasing popularity of online social networks (OSNs) has made the propagation of privacy information in such networks a great concern. This paper aims to provide an in-depth study to reveal some main characteristics as well as the impacting factors on the propagation of privacy information in OSNs so as to establish a scientific basis for the development of privacy protection policies and mechanisms. Challenges in the construction of privacy information propagation models include a proper definition of privacy information and a precise characterization of the propagation. To realize the goals, in this study, we first provide a definition of privacy information and then propose a method for the reconstruction of the propagation paths of privacy information in Weibo (W-PIPPR), one of the most popular OSNs in China ( https://weibo.com ), based on which a dataset for privacy information propagation (PIPD-Weibo) has been constructed. In addition, we conducted an assessment on general perceptions of the sensitivity of various privacy attributes based on the questionnaire “What is your privacy?” that we designed and distributed. Analysis performed on PIPD-Weibo revealed the speed and scale as well as the topological structure of the propagation, showing that the influence of the privacy subjects as well as the sensitivity of private attributes is significant on the speed and scale of the propagation. Our study can not only provide some insight understanding of the propagation of privacy information in OSNs, but also contribute to accumulating empirical cases for the research on the propagation of privacy information in OSNs. Besides, our study has some practical implications on the design of software for privacy information propagation in OSNs and can aid the development of effective cybersecurity and privacy protection policies and strategies in OSNs.