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 ...

Abstract—Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”).

Abstract—Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”).

Sep 4, 2015 · In order to be effective, a location-privacy preserving mechanism must consider both the privacy and utility requirements of each user, as well ...

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 ...

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 ...

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 ...

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 ...

This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service ...

Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”).