File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: I know where you all are! exploiting mobile social apps for large-scale location privacy probing

TitleI know where you all are! exploiting mobile social apps for large-scale location privacy probing
Authors
Issue Date2016
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9722, p. 3-19 How to Cite?
Abstract© Springer International Publishing Switzerland 2016. Mobile social apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, Location-Based Social Network (LBSN) apps (e.g., Wechat, SayHi, Momo) that encourage people to make friends with nearby strangers have gained their popularity recently. They provide a “Nearby” feature for a user to find other users near him/her. While seeing other users, the user, as well as his/her coarse-grained relative location, will also be visible in the “Nearby” feature of other users. Leveraging this observation, in this paper, we model the location probing attacks, and propose three approaches to perform large-scale such attacks on LBSN apps. Moreover, we apply the new approaches in the risk assessment of eight popular LBSN apps, each of which has millions of installation. The results demonstrate the severity of such attacks. More precisely, our approaches can collect a huge volume of users’ location information effectively and automatically, which can be exploited to invade users’ privacy. This study sheds light on the research of protecting users’ private location information.
Persistent Identifierhttp://hdl.handle.net/10722/280588
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhao, Shuang-
dc.contributor.authorLuo, Xiapu-
dc.contributor.authorBai, Bo-
dc.contributor.authorMa, Xiaobo-
dc.contributor.authorZou, Wei-
dc.contributor.authorQiu, Xinliang-
dc.contributor.authorAu, Man Ho-
dc.date.accessioned2020-02-17T14:34:25Z-
dc.date.available2020-02-17T14:34:25Z-
dc.date.issued2016-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9722, p. 3-19-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/280588-
dc.description.abstract© Springer International Publishing Switzerland 2016. Mobile social apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, Location-Based Social Network (LBSN) apps (e.g., Wechat, SayHi, Momo) that encourage people to make friends with nearby strangers have gained their popularity recently. They provide a “Nearby” feature for a user to find other users near him/her. While seeing other users, the user, as well as his/her coarse-grained relative location, will also be visible in the “Nearby” feature of other users. Leveraging this observation, in this paper, we model the location probing attacks, and propose three approaches to perform large-scale such attacks on LBSN apps. Moreover, we apply the new approaches in the risk assessment of eight popular LBSN apps, each of which has millions of installation. The results demonstrate the severity of such attacks. More precisely, our approaches can collect a huge volume of users’ location information effectively and automatically, which can be exploited to invade users’ privacy. This study sheds light on the research of protecting users’ private location information.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.titleI know where you all are! exploiting mobile social apps for large-scale location privacy probing-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-40253-6_1-
dc.identifier.scopuseid_2-s2.0-84978216578-
dc.identifier.volume9722-
dc.identifier.spage3-
dc.identifier.epage19-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000386508700001-
dc.identifier.issnl0302-9743-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats