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Abstract

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 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 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.

Item Type:	Article
Authors/Creators:	Aghasian, E and Garg, S and Gao, L and Yu, S and Montgomery, J
Keywords:	privacy, social networks, measurement, fuzzy logic
Journal or Publication Title:	IEEE Access
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2169-3536
DOI / ID Number:	https://doi.org/10.1109/ACCESS.2017.2720187
Copyright Information:	Copyright 2017 IEEE.
Related URLs:	Google Scholar: Garg, S UTAS Profile: Garg, S Google Scholar: Montgomery, J UTAS Profile: Montgomery, J
Item Statistics:	View statistics for this item

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