Christin, Delphine ; Bentolila, A. ; Hollick, Matthias (2012)
Friend is Calling: Exploiting Mobile Phone Data to Help Users in Setting their Privacy Preferences.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
A continuously increasing number of pictures and videos is being shared in online social networks. Currently, users manually confine access to the contents shared. This configuration process can rapidly become cumbersome for users sharing a large amount of content, and, as a result, they may be tempted to rush through the process or leave the default settings unchanged. This can seriously endanger their privacy if inappropriate users are authorized to access sensitive data. In order to reduce the burden on the users as well as enhance their privacy protection, we propose to leverage in- formation already available on their mobile phone as a basis for recommendations on how to set their privacy preferences. To this end, we conducted a user study exploring the differences between users belonging to different social groups, in terms of communication patterns. We have designed clas- sifiers based on mobile phone data to distinguish members of different social groups, and we have evaluated these clas- sifiers using a real-world dataset. The results show that friends can be easily identified using call and short messages logs, while identifying colleagues requires additional information.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Christin, Delphine ; Bentolila, A. ; Hollick, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Friend is Calling: Exploiting Mobile Phone Data to Help Users in Setting their Privacy Preferences |
| Sprache: | Deutsch |
| Publikationsjahr: | Juni 2012 |
| Buchtitel: | Proceedings of the 4th International Workshop on Security and Privacy in Spontaneous Interaction and Mobile Phone Use (IWSSI/SPMU) |
| Kurzbeschreibung (Abstract): | A continuously increasing number of pictures and videos is being shared in online social networks. Currently, users manually confine access to the contents shared. This configuration process can rapidly become cumbersome for users sharing a large amount of content, and, as a result, they may be tempted to rush through the process or leave the default settings unchanged. This can seriously endanger their privacy if inappropriate users are authorized to access sensitive data. In order to reduce the burden on the users as well as enhance their privacy protection, we propose to leverage in- formation already available on their mobile phone as a basis for recommendations on how to set their privacy preferences. To this end, we conducted a user study exploring the differences between users belonging to different social groups, in terms of communication patterns. We have designed clas- sifiers based on mobile phone data to distinguish members of different social groups, and we have evaluated these clas- sifiers using a real-world dataset. The results show that friends can be easily identified using call and short messages logs, while identifying colleagues requires additional information. |
| Freie Schlagworte: | Secure Things |
| ID-Nummer: | TUD-CS-2012-0120 |
| Fachbereich(e)/-gebiet(e): | LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Hinterlegungsdatum: | 31 Dez 2016 11:08 |
| Letzte Änderung: | 02 Jul 2021 09:56 |
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