In this paper, we explore the risks of friends in social networks caused by their friendship patterns, by using real life social network data and starting from a previously defined risk model. Particularly, we observe that risks of friendships can be mined by analyzing users' attitude towards friends of friends. This allows us to give new insights into friendship and risk dynamics on social networks.
An advantage of global models is that once they are learned, privacy settings can be transfered and applied to other users. In such a shared privacy work, @cite_20 use of privacy settings which are specified by friends or trusted experts. However, the authors do not use a global risk privacy model, and users should know which suites to use without knowing the risk of social network users surrounding him her.
@cite_20
1981743035
Creating privacy controls for social networks that are both expressive and usable is a major challenge. Lack of user understanding of privacy settings can lead to unwanted disclosure of private information and, in some cases, to material harm. We propose a new paradigm which allows users to easily choose “suites” of privacy settings which have been specified by friends or trusted experts, only modifying them if they wish. Given that most users currently stick with their default, operator-chosen settings, such a system could dramatically increase the privacy protection that most users experience with minimal time investment.