Empowering Mobile Crowdsourcing Apps with ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Empowering Mobile Crowdsourcing Apps with User Privacy Control
Author(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]
Journal title :
Journal of Parallel and Distributed Computing
Pages :
15
Publisher :
Elsevier
Publication date :
2020-08-01
ISSN :
0743-7315
English keyword(s) :
Distributed Applications
Location Privacy
Mobile Crowdsourcing
Location Privacy Protection Mechanism
Location Privacy
Mobile Crowdsourcing
Location Privacy Protection Mechanism
HAL domain(s) :
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Informatique mobile
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Informatique mobile
English abstract : [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’ ...
Show more >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 metadata (e.g., time and location stamps), which may inevitably lead to user privacy leaks by discarding sensitive information in the wild.This article addresses this critical limitation of the state of the art by proposing a software library that empower legacy mobile crowsourcing apps to increase 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 crowdsourced datasets.Show less >
Show more >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 metadata (e.g., time and location stamps), which may inevitably lead to user privacy leaks by discarding sensitive information in the wild.This article addresses this critical limitation of the state of the art by proposing a software library that empower legacy mobile crowsourcing apps to increase 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 crowdsourced datasets.Show less >
Language :
Anglais
Popular science :
Non
ANR Project :
Collections :
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