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Issue No. 04 - April (2018 vol. 17)
ISSN: 1536-1233
pp: 760-774
Kevin Huguenin , Faculty of Business and Economics Lausanne, University of Lausanne, Lausanne, Switzerland
Igor Bilogrevic , Google, Zurich, Switzerland
Joana Soares Machado , École Ploytechnique Fédérale de Lausanne, Lausanne, Switzerland
Stefan Mihaila , École Ploytechnique Fédérale de Lausanne, Lausanne, Switzerland
Reza Shokri , Computer Science Department, National University of Singapore, Singapore, Singapore
Italo Dacosta , École Ploytechnique Fédérale de Lausanne, Lausanne, Switzerland
Jean-Pierre Hubaux , École Ploytechnique Fédérale de Lausanne, Lausanne, Switzerland
Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the users’ motivations behind their location check-ins, based on a study targeted at Foursquare users ($_$N = 77$_$ ). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in [43%] of the cases and in [63%] of the cases for the coarse-grained motivation. It also predicts, with a mean error of [0.52] (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement location-sharing mechanisms that preserve both the utility and privacy for their users.
Semantics, Privacy, Predictive models, Social network services, Mobile computing, Data privacy

K. Huguenin et al., "A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins," in IEEE Transactions on Mobile Computing, vol. 17, no. 4, pp. 760-774, 2018.
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