Deriving privacy settings for location sharing: Are context factors always the best choice?

Frederic Raber, Antonio Krüger

In: Xiuzhen Cheng, Nan Zhang, Joseph (Joe) Valacich (Hrsg.). Proceedings of the 2nd IEEE Symposium on Privacy-Aware Computing. IEEE Symposium on Privacy-Aware Computing (PAC-18) September 26-28 Washington DC United States IEEE 2018.


Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.


proceedings.pdf (pdf, 155 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence