ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility

Détails

ID Serval
serval:BIB_D928FEEEF483
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility
Titre de la conférence
Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management
Auteur(s)
Moro A., Garbinato B.
Editeur
SCITEPRESS - Science and Technology Publications
Adresse
Funchal, Madeira, Portugal
ISBN
9789897582943
Statut éditorial
Publié
Date de publication
2018
Volume
1
Pages
107-118
Langue
anglais
Résumé
Location prediction and location privacy has retained a lot of attention recent years. Predicting locations is the next step of Location-Based Services (LBS) because it provides information not only based on where you are but where you will be. However, obtaining information from LBS has a price for the user because she must share all her locations with the service that builds a predictive model, resulting in a loss of privacy. In this paper we propose ResPred, a system that allows LBS to request location prediction about the user. The system includes a location prediction component containing a statistical location trend model and a location privacy component aiming at blurring the predicted locations by finding an appropriate tradeoff between LBS utility and user privacy, the latter being expressed as a maximum percentage of utility loss. We evaluate ResPred from a utility/privacy perspective by comparing our privacy mechanism with existing techniques by using real user locations.
Création de la notice
20/07/2018 14:21
Dernière modification de la notice
21/08/2019 5:11
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