Reinhardt, Delphine ; Engelmann, Franziska ; Moerov, Andrey ; Hollick, Matthias (2015)
Show Me Your Phone, I Will Tell You Who Your Friends Are: Analyzing Smartphone Data to Identify Social Relationships.
Linz, Austria
doi: 10.1145/2836041.2836048
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Access control is a key principle to protect user privacy online. The combination of both the wealth of user-generated data in online social networks and overly complex user interfaces lead to a high user burden for privacy control, hence making the observance of the above principles difficult. We investigate how communication metadata on smartphones can facilitate providing tailored suggestions for restricted audience groups, thus limiting the sharing of data to the intended users only. To this end, we have performed a user study collecting a dataset including contact names, calls, SMS, MMS, and e-mail on personal smartphones in everyday use. In this paper, we examine which are the key features determining the social relationship category of a contact using machine learning. We obtain promising results for an automated classification of contacts into work-related, family-related and other social-interaction-related, thus enabling the possibility of user assistance for privacy control. Obtaining a more fine-grained categorization of the latter category into acquaintances, friends, and university-mates is shown to be difficult, since these categories blur in our study group.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Reinhardt, Delphine ; Engelmann, Franziska ; Moerov, Andrey ; Hollick, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Show Me Your Phone, I Will Tell You Who Your Friends Are: Analyzing Smartphone Data to Identify Social Relationships |
| Sprache: | Deutsch |
| Publikationsjahr: | 2015 |
| Verlag: | ACM |
| Buchtitel: | 14th International Conference on Mobile and Ubiquitous Multimedia (MUM) |
| Veranstaltungsort: | Linz, Austria |
| DOI: | 10.1145/2836041.2836048 |
| Kurzbeschreibung (Abstract): | Access control is a key principle to protect user privacy online. The combination of both the wealth of user-generated data in online social networks and overly complex user interfaces lead to a high user burden for privacy control, hence making the observance of the above principles difficult. We investigate how communication metadata on smartphones can facilitate providing tailored suggestions for restricted audience groups, thus limiting the sharing of data to the intended users only. To this end, we have performed a user study collecting a dataset including contact names, calls, SMS, MMS, and e-mail on personal smartphones in everyday use. In this paper, we examine which are the key features determining the social relationship category of a contact using machine learning. We obtain promising results for an automated classification of contacts into work-related, family-related and other social-interaction-related, thus enabling the possibility of user assistance for privacy control. Obtaining a more fine-grained categorization of the latter category into acquaintances, friends, and university-mates is shown to be difficult, since these categories blur in our study group. |
| ID-Nummer: | TUD-CS-2015-12053 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Sichere Mobile Netze |
| Hinterlegungsdatum: | 31 Dez 2016 11:08 |
| Letzte Änderung: | 10 Jun 2021 06:12 |
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