FOUGERE: User-Centric Location Privacy in ...
Type de document :
Communication dans un congrès avec actes
Titre :
FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps
Auteur(s) :
Meftah, Lakhdar [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Chrisment, Isabelle [Auteur]
Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems [RESIST]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Chrisment, Isabelle [Auteur]
Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems [RESIST]
Éditeur(s) ou directeur(s) scientifique(s) :
José Pereira
Laura Ricci
Laura Ricci
Titre de la manifestation scientifique :
DAIS 2019 - 19th IFIP International Conference on Distributed Applications and Interoperable Systems
Ville :
Kongens Lyngby
Pays :
Danemark
Date de début de la manifestation scientifique :
2019-06-17
Titre de l’ouvrage :
Lecture Notes in Computer Science
Titre de la revue :
Distributed Applications and Interoperable Systems
Éditeur :
Springer International Publishing
Date de publication :
2019
Mot(s)-clé(s) en anglais :
LPPM
mobile crowdsourcing
Location privacy
mobile crowdsourcing
Location privacy
Discipline(s) HAL :
Informatique [cs]/Système d'exploitation [cs.OS]
Informatique [cs]/Web
Informatique [cs]/Informatique mobile
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]
Informatique [cs]/Web
Informatique [cs]/Informatique mobile
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]
Résumé en anglais : [en]
Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants' ...
Lire la suite >Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants' activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with time and location stamps, which may inevitably lead to user privacy leaks by discarding sensitive information. This paper addresses this critical limitation of the state of the art by proposing a software library that improves user privacy without compromising the overall quality of the crowdsourced datasets. We propose a decentralized approach, named Fougere, to convey data samples from user devices to third-party servers. By introducing an a priori data anonymization process, we show that Fougere defeats state-of-the-art location-based privacy attacks with little impact on the quality of crowd-sourced datasets.Lire moins >
Lire la suite >Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants' activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with time and location stamps, which may inevitably lead to user privacy leaks by discarding sensitive information. This paper addresses this critical limitation of the state of the art by proposing a software library that improves user privacy without compromising the overall quality of the crowdsourced datasets. We propose a decentralized approach, named Fougere, to convey data samples from user devices to third-party servers. By introducing an a priori data anonymization process, we show that Fougere defeats state-of-the-art location-based privacy attacks with little impact on the quality of crowd-sourced datasets.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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