Taxonomy of social network data types

نویسندگان

  • Christian Richthammer
  • Michael Netter
  • Moritz Riesner
  • Johannes Sänger
  • Günther Pernul
چکیده

Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.

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عنوان ژورنال:
  • EURASIP J. Information Security

دوره 2014  شماره 

صفحات  -

تاریخ انتشار 2014