Shared data granularity: A latent dimension of privacy scoring over online social networks
This work addresses privacy risk assessment for users of professional online social networks, but it is incremental as it builds on existing scoring methods with a new data source.
The paper tackled the problem of privacy scoring in online social networks by analyzing real-world LinkedIn data from 5,389 users and proposing a novel method based on data granularity, showing its effectiveness through experimental evaluation.
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 personel 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, modelled 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 consider 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.