Using User Behavior to Measure Privacy on Online Social Networks

Because social networks exemplify the phenomenon of homogeneity in complex networks, researchers generally believe that a user’s privacy disclosure is closely related to that of the users around them, but we find that the related users studied in previous methods were not correct. That is, the analyzed user groups may have had nothing to do with the privacy disclosure of the target users. Since private information is time-sensitive, information held by users who are no longer in the same environment as the target user may no longer be true and have lost its value. For example, considering students and members of the working class, transfers to another school for further studies or job changes entail dramatic changes to most of their information. This lack of timeliness has an overarching impact on the effectiveness of social network analysis and privacy protection, but this problem has not been addressed by researchers. Therefore, we study and characterize this problem, add the user’s behavior trace to solve this problem and measure the user’s privacy status more accurately.