Abstract
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.
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Data availability
We construct a Weibo-based dataset and make it publicly available for future research on the study of propagation of privacy information (PIPD-Weibo) that contains 30 instances of privacy information. PIPD-Weibo can be accessed via https://github.com/honglalala/PIPD--Weibo.
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Acknowledgements
The work presented in this paper has been supported by Beijing Natural Science Foundation (No. IS23054), Science Foundation of China University of Petroleum, Beijing (No. 2462024SZBH007) and by the 2023 International Cooperation Training Program for Innovative Talents (“Double First-class” Construction Special Program - “Artificial Intelligence + Internet of Things”) of the China Scholarship Council (CSC).
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Jingsha He and Nafei Zhu proposed the conceptualization and methodology, Yehong Luo wrote the manuscript text, conducted experiments, collated and analyzed the data, Lei Sun and Ziwen Wang did some experiments, Yuzi Yi and Xiangjun Ma collated the data, Jurcut, Anca Delia revised the manuscript. All authors reviewed the manuscript.
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Appendix
Appendix
Questionnaires are generally used as a method of research to uncover facts and to provide some insights into what individuals really think about something. The definition of privacy stresses that the judgment on privacy by humans is subjective, i.e., humans are the decision makers regarding what privacy is. Consequently, we decided to design a questionnaire and ask participants to tell us the importance of private attributes from their perspectives. The design of the questionnaire “What is your privacy?” consists mainly of three parts.
The first part aims to collect the basic information of the participants, which includes: age, gender, occupation, education level and marital status. Since the judgment of privacy is personal, participants of the survey should span across as many different walks of life as possible to ensure the diversity of the collected data. Different ages and different occupations also matter greatly. For example, politicians would generally prefer keeping their contact information confidential while teachers often publish such information on their websites. Gender, education and marital status would greatly influence the privacy awareness of individuals.
The second part aims to collect the privacy opinions of the participants on the set of private attributes that we include in the questionnaire based on the common belief that humans would generally agree on a common set of private attributes. Caliskan et al. categorized personal attributes into nine categories based on information posted in tweets [28]. In our design, we summarized the private attributes into eight categories after removing “neutral descriptions”. Participants could also augment the questionnaire with private attributes that they considered to be relevant.
The third part aims to collect information on how the participants feel about the sensitivity of each private attribute in the questionnaire. For each choice of the private attributes by a participant, an assessment of the likelihood of releasing the private attribute was sought. The likelihood in the questionnaire employed a 5-point scale from “I will not release it” to “I will definitely release it” with lower values indicating higher levels of the willingness to conceal and higher values indicating higher levels of the willingness to release. The general framework as well as the design of the questionnaire is shown in Fig. 14.
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Luo, Y., Zhu, N., Wang, Z. et al. Privacy information propagation in online social networks - a case study based on Weibo data. Int. J. Inf. Secur. 24, 32 (2025). https://doi.org/10.1007/s10207-024-00946-5
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DOI: https://doi.org/10.1007/s10207-024-00946-5