Privacy measurement method using a graph structure on online social networks

被引:5
作者
Li, XueFeng [1 ,2 ]
Zhao, Chensu [2 ,3 ]
Tian, Keke [4 ]
机构
[1] Henan Univ Technol, Sch Art Intelligence & Big Data, Zhengzhou, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Cybersp Secur, Beijing, Peoples R China
[3] Shandong Yingcai Univ, Sch Informat & Engn, Jinan, Peoples R China
[4] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Henan, Peoples R China
关键词
Attribute content; graph structure; online social networks; privacy literacy; privacy measurement; similarity; FRAMEWORK; DISCLOSURE; SECURITY;
D O I
10.4218/etrij.2019-0495
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable.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.
引用
收藏
页码:812 / 824
页数:13
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