%0 Conference Proceedings
%T FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps
%+ Self-adaptation for distributed services and large software systems (SPIRALS)
%+ Institut universitaire de France (IUF)
%+ Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems (RESIST)
%A Meftah, Lakhdar
%A Rouvoy, Romain
%A Chrisment, Isabelle
%Z Inria Project Lab BetterNet BetterNet
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B DAIS 2019 - 19th IFIP International Conference on Distributed Applications and Interoperable Systems
%C Kongens Lyngby, Denmark
%Y José Pereira
%Y Laura Ricci
%I Springer International Publishing
%3 Distributed Applications and Interoperable Systems
%V 11534
%P 116-132
%8 2019-06-17
%D 2019
%R 10.1007/978-3-030-22496-7_8
%K LPPM
%K mobile crowdsourcing
%K Location privacy
%Z Computer Science [cs]/Operating Systems [cs.OS]
%Z Computer Science [cs]/Web
%Z Computer Science [cs]/Mobile Computing
%Z Computer Science [cs]/Ubiquitous Computing
%Z Computer Science [cs]/Software Engineering [cs.SE]
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z IPL Betternet
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-02121311/document
%2 https://inria.hal.science/hal-02121311/file/paper_2.pdf
%L hal-02121311
%U https://inria.hal.science/hal-02121311
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