Scoring Users' Privacy Disclosure Across Multiple Online Social Networks

Aghasian, E; Garg, S; Gao, L; Yu, S; Montgomery, J

Scoring Users' Privacy Disclosure Across Multiple Online Social Networks

Aghasian, E Garg, S Gao, L Yu, S

Montgomery, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2017, 5, pp. 13118-13130
Issue Date:: 2017-06-25

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Field	Value	Language
dc.contributor.author	Aghasian, E
dc.contributor.author	Garg, S
dc.contributor.author	Gao, L
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Montgomery, J
dc.date.accessioned	2021-04-29T00:28:16Z
dc.date.available	2021-04-29T00:28:16Z
dc.date.issued	2017-06-25
dc.identifier.citation	IEEE Access, 2017, 5, pp. 13118-13130
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/148531
dc.description.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 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.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2017.2720187
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Scoring Users' Privacy Disclosure Across Multiple Online Social Networks
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-29T00:28:15Z
pubs.publication-status	Published
pubs.volume	5

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 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.

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http://hdl.handle.net/10453/148531