Scoring Users' 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. Curr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2017-01, Vol.5, p.13118-13130
Hauptverfasser: Aghasian, Erfan, Garg, Saurabh, Longxiang Gao, Shui Yu, Montgomery, James
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 13130
container_issue
container_start_page 13118
container_title IEEE access
container_volume 5
creator Aghasian, Erfan
Garg, Saurabh
Longxiang Gao
Shui Yu
Montgomery, James
description 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.
doi_str_mv 10.1109/ACCESS.2017.2720187
format Article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2455943788</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7959160</ieee_id><doaj_id>oai_doaj_org_article_4b1881e453dc478fbba9ca9bd9e9beb3</doaj_id><sourcerecordid>2455943788</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-1372c001694947fae7cc2221236d6c2a4f07b66b71d96e973f478681d1a8e9703</originalsourceid><addsrcrecordid>eNpNUclOwzAQjRBIVIUv4BKJA6cWb_FyrEKhSGxS4Ww5zqRyCXWxE1D_HkOqirnMonnvjeZl2QVGU4yRup6V5Xy5nBKExZSIlKQ4ykYEczWhBeXH_-rT7DzGNUoh06gQo2yxtD64zSp_ixDiVf4S3Jexu_zGRdv62AfIZzb4GPPHvu3ctoX8edO6DeRLb51p8yfovn14j2fZSWPaCOf7PM7ebuev5WLy8Hx3X84eJpYh2U0wFcQilNSZYqIxIKwlhGBCec0tMaxBouK8ErhWHJSgDROSS1xjI1OL6Di7H3hrb9Z6G9yHCTvtjdN_Ax9W2oTO2RY0q7CUGFhBa5tYmqoyyhpV1QpUBRVNXJcD1zb4zx5ip9e-D5t0viasKBSjQsq0RYetvz8EaA6qGOlfB_TggP51QO8dSKiLAeUA4IAQqlCYI_oD3muAmw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455943788</pqid></control><display><type>article</type><title>Scoring Users' Privacy Disclosure Across Multiple Online Social Networks</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Aghasian, Erfan ; Garg, Saurabh ; Longxiang Gao ; Shui Yu ; Montgomery, James</creator><creatorcontrib>Aghasian, Erfan ; Garg, Saurabh ; Longxiang Gao ; Shui Yu ; Montgomery, James</creatorcontrib><description>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.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2017.2720187</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Computational modeling ; Data models ; Data privacy ; Digital media ; Facebook ; fuzzy logic ; Fuzzy systems ; measurement ; Privacy ; Sensitivity ; Social networks ; Visibility</subject><ispartof>IEEE access, 2017-01, Vol.5, p.13118-13130</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-1372c001694947fae7cc2221236d6c2a4f07b66b71d96e973f478681d1a8e9703</citedby><cites>FETCH-LOGICAL-c408t-1372c001694947fae7cc2221236d6c2a4f07b66b71d96e973f478681d1a8e9703</cites><orcidid>0000-0002-5232-4934</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7959160$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,861,2096,27614,27905,27906,54914</link.rule.ids></links><search><creatorcontrib>Aghasian, Erfan</creatorcontrib><creatorcontrib>Garg, Saurabh</creatorcontrib><creatorcontrib>Longxiang Gao</creatorcontrib><creatorcontrib>Shui Yu</creatorcontrib><creatorcontrib>Montgomery, James</creatorcontrib><title>Scoring Users' Privacy Disclosure Across Multiple Online Social Networks</title><title>IEEE access</title><addtitle>Access</addtitle><description>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.</description><subject>Computational modeling</subject><subject>Data models</subject><subject>Data privacy</subject><subject>Digital media</subject><subject>Facebook</subject><subject>fuzzy logic</subject><subject>Fuzzy systems</subject><subject>measurement</subject><subject>Privacy</subject><subject>Sensitivity</subject><subject>Social networks</subject><subject>Visibility</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUclOwzAQjRBIVIUv4BKJA6cWb_FyrEKhSGxS4Ww5zqRyCXWxE1D_HkOqirnMonnvjeZl2QVGU4yRup6V5Xy5nBKExZSIlKQ4ykYEczWhBeXH_-rT7DzGNUoh06gQo2yxtD64zSp_ixDiVf4S3Jexu_zGRdv62AfIZzb4GPPHvu3ctoX8edO6DeRLb51p8yfovn14j2fZSWPaCOf7PM7ebuev5WLy8Hx3X84eJpYh2U0wFcQilNSZYqIxIKwlhGBCec0tMaxBouK8ErhWHJSgDROSS1xjI1OL6Di7H3hrb9Z6G9yHCTvtjdN_Ax9W2oTO2RY0q7CUGFhBa5tYmqoyyhpV1QpUBRVNXJcD1zb4zx5ip9e-D5t0viasKBSjQsq0RYetvz8EaA6qGOlfB_TggP51QO8dSKiLAeUA4IAQqlCYI_oD3muAmw</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Aghasian, Erfan</creator><creator>Garg, Saurabh</creator><creator>Longxiang Gao</creator><creator>Shui Yu</creator><creator>Montgomery, James</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5232-4934</orcidid></search><sort><creationdate>20170101</creationdate><title>Scoring Users' Privacy Disclosure Across Multiple Online Social Networks</title><author>Aghasian, Erfan ; Garg, Saurabh ; Longxiang Gao ; Shui Yu ; Montgomery, James</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-1372c001694947fae7cc2221236d6c2a4f07b66b71d96e973f478681d1a8e9703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computational modeling</topic><topic>Data models</topic><topic>Data privacy</topic><topic>Digital media</topic><topic>Facebook</topic><topic>fuzzy logic</topic><topic>Fuzzy systems</topic><topic>measurement</topic><topic>Privacy</topic><topic>Sensitivity</topic><topic>Social networks</topic><topic>Visibility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aghasian, Erfan</creatorcontrib><creatorcontrib>Garg, Saurabh</creatorcontrib><creatorcontrib>Longxiang Gao</creatorcontrib><creatorcontrib>Shui Yu</creatorcontrib><creatorcontrib>Montgomery, James</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aghasian, Erfan</au><au>Garg, Saurabh</au><au>Longxiang Gao</au><au>Shui Yu</au><au>Montgomery, James</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scoring Users' Privacy Disclosure Across Multiple Online Social Networks</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2017-01-01</date><risdate>2017</risdate><volume>5</volume><spage>13118</spage><epage>13130</epage><pages>13118-13130</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2017.2720187</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-5232-4934</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2169-3536
ispartof IEEE access, 2017-01, Vol.5, p.13118-13130
issn 2169-3536
2169-3536
language eng
recordid cdi_proquest_journals_2455943788
source IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Computational modeling
Data models
Data privacy
Digital media
Facebook
fuzzy logic
Fuzzy systems
measurement
Privacy
Sensitivity
Social networks
Visibility
title Scoring Users' Privacy Disclosure Across Multiple Online Social Networks
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T03%3A12%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Scoring%20Users'%20Privacy%20Disclosure%20Across%20Multiple%20Online%20Social%20Networks&rft.jtitle=IEEE%20access&rft.au=Aghasian,%20Erfan&rft.date=2017-01-01&rft.volume=5&rft.spage=13118&rft.epage=13130&rft.pages=13118-13130&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2017.2720187&rft_dat=%3Cproquest_ieee_%3E2455943788%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2455943788&rft_id=info:pmid/&rft_ieee_id=7959160&rft_doaj_id=oai_doaj_org_article_4b1881e453dc478fbba9ca9bd9e9beb3&rfr_iscdi=true