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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:	Refereed Article
Keywords:	privacy, social networks, measurement, fuzzy logic
Research Division:	Information and Computing Sciences
Research Group:	Cybersecurity and privacy
Research Field:	Cybersecurity and privacy not elsewhere classified
Objective Division:	Information and Communication Services
Objective Group:	Information services
Objective Field:	Information services not elsewhere classified
UTAS Author:	Aghasian, E (Mr Erfan Aghasian)
UTAS Author:	Garg, S (Dr Saurabh Garg)
UTAS Author:	Montgomery, J (Dr James Montgomery)
ID Code:	117598
Year Published:	2017
Web of Science® Times Cited:	34
Deposited By:	Information and Communication Technology
Deposited On:	2017-06-20
Last Modified:	2018-06-14
Downloads:	129 View Download Statistics