A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Details

Ressource 1Download: HugueninTMC2018.pdf (1542.29 [Ko])
State: Public
Version: author
Serval ID
serval:BIB_1B7C37DE404A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins
Journal
IEEE Transactions on Mobile Computing
Author(s)
Huguenin K., Bilogrevic I., Machado Soares J., Mihaila S., Shokri R., Dacosta I., Hubaux J.-P.
ISSN
1536-1233
Publication state
Published
Issued date
01/04/2018
Peer-reviewed
Oui
Volume
17
Number
4
Pages
760-774
Language
english
Abstract
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.
Keywords
Computer Networks and Communications, Electrical and Electronic Engineering, Software, Computer Networks and Communications, Electrical and Electronic Engineering, Software
Open Access
Yes
Create date
14/08/2017 12:36
Last modification date
21/11/2022 8:16
Usage data