serval:BIB_78C0943DCA20
Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms
10.14722/ndss.2015.23032
Bilogrevic
I.
author
Huguenin
K.
author
Mihaila
S.
author
Shokri
R.
author
Hubaux
J.-P.
author
inproceedings
2015
Internet Society
San Diego, CA, USA
Proceedings of the 22nd Network and Distributed System Security Symposium (NDSS)
1-891562-38-X
conference publication
NA
Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their privacy is threatened. In this paper, we study users’ motivations behind location check-ins, and we quantify the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. By means of a targeted user study on Foursquare (N = 77), we show that the motivation behind Foursquare check-ins is a mediator of the loss of utility caused by generalization. Using these findings, we propose a machine learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for utility. Our results show that the model accurately predicts the loss of utility caused by semantic and geographical generalization; this model enables the design of utility-aware, privacy-enhancing mechanisms in location-based social networks.
eng
60_published
true
peer-reviewed
University of Lausanne
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