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Scoring users' privacy disclosure across multiple online social networks

Aghasian, Erfan, Garg, Saurabh, Gao, Longxiang, Yu, Shui and Montgomery, James 2017, Scoring users' privacy disclosure across multiple online social networks, IEEE access, vol. 5, pp. 13118-13130, doi: 10.1109/ACCESS.2017.2720187.

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Title Scoring users' privacy disclosure across multiple online social networks
Author(s) Aghasian, Erfan
Garg, Saurabh
Gao, LongxiangORCID iD for Gao, Longxiang orcid.org/0000-0002-3026-7537
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Montgomery, James
Journal name IEEE access
Volume number 5
Start page 13118
End page 13130
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-06
ISSN 2169-3536
Keyword(s) privacy
social networks
measurement
fuzzy logic
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
INFORMATION-PRIVACY
Summary 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.
Language eng
DOI 10.1109/ACCESS.2017.2720187
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30097469

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.