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- W2108436124 abstract "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." @default.
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- W2108436124 date "2015-01-01" @default.
- W2108436124 modified "2023-10-17" @default.
- W2108436124 title "Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms" @default.
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- W2108436124 doi "https://doi.org/10.14722/ndss.2015.23032" @default.
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