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http://dx.doi.org/10.4218/etrij.2019-0495

Privacy measurement method using a graph structure on online social networks  

Li, XueFeng (School of Artificial Intelligence and Big Data, Henan University of Technology)
Zhao, Chensu (School of Cyberspace Security, Beijing University of Posts and Telecommunications)
Tian, Keke (School of Computer Science and Technology, Henan Polytechnic University)
Publication Information
ETRI Journal / v.43, no.5, 2021 , pp. 812-824 More about this Journal
Abstract
Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.
Keywords
attribute content; graph structure; online social networks; privacy literacy; privacy measurement; similarity;
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