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Scoring users' privacy disclosure across multiple online social networks

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journal contribution
posted on 2017-06-01, 00:00 authored by Erfan Aghasian, S Garg, Longxiang Gao, Shui Yu, J Montgomery
Users in online social networking sites unknowingly disclose their sensitive information that aggravate the social and financial risks. Hence, to prevent the information loss and privacy exposure, users need to find ways to quantify their privacy level based on their online social network data. Current studies that focus on measuring the privacy risk and disclosure consider only a single source of data, neglecting the fact that users, in general, can have multiple social network accounts disclosing different sensitive information. In this paper, we investigate an approach that can help social media users to measure their privacy disclosure score (PDS) based on the information shared across multiple social networking sites. In particular, we identify the main factors that have impact on users privacy, namely, sensitivity and visibility, to obtain the final disclosure score for each user. By applying the statistical and fuzzy systems, we can specify the potential information loss for a user by using obtained PDS. Our evaluation results with real social media data show that our method can provide a better estimation of privacy disclosure score for users having presence in multiple online social networks.



IEEE access




13118 - 13130




Piscataway, N.J.





Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, IEEE