Scoring users\u27 privacy disclosure across multiple online social networks

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

ARE YOU REALLY HIDDEN? ESTIMATING CURRENT CITY EXPOSURE RISK IN ONLINE SOCIAL NETWORKS

Nowadays, Online Social Networks (OSNs) become more and more concerned about users’ privacy issues, and put more eorts to protect users from being violated by privacy breaches (e.g., spamming, deceptive advertising). Although OSNs encourage users to hide their private information, the users may not be really protected as the hidden information could still be predicted from other public information. This paper, taking a particular privacy-sensitive attribute ‘current city’ in Facebook as a representative, aims to notify individual users of the quantified exposure risk that their hidden attributes can be correctly predicted, and also provide them with countermeasures. Specifically, we first design a current city prediction approach that infers users’ hidden current city from their self-exposed information. Based on 371;913 Facebook users’ data, we verify that our proposed prediction approach can outperform state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable features of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure probability/risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection; while the extension to a general attribute exposure estimator is also discussed to facilitate OSNs to maintain a healthy social and business environment