Shared data granularity: a latent dimension of privacy scoring over online social networks

Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN). Existing work in the field rely on possibly biased or emotional survey data and focus only on personal purpose OSNs like Facebook. In contrast to existing work, in this thesis, we work with real-world OSN data collected from LinkedIn, the most popular professional-purpose OSN (ProOSN). Towards this end, we developed an extensive crawler to collect all relevant profile data of 5,389 LinkedIn users, modeled these data using both relational and graph databases and quantitatively analyzed all privacy risk scoring methods in the literature. Additionally, we propose a novel scoring method that considers the granularity of data an OSN user shares on her profile page. Extensive experimental evaluation of existing and proposed scoring methods indicates the effectiveness of the proposed solution.