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Conference Papers Year : 2019

FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps

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Abstract

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.
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Dates and versions

hal-02121311 , version 1 (06-05-2019)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Lakhdar Meftah, Romain Rouvoy, Isabelle Chrisment. FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps. DAIS 2019 - 19th IFIP International Conference on Distributed Applications and Interoperable Systems, Jun 2019, Kongens Lyngby, Denmark. pp.116-132, ⟨10.1007/978-3-030-22496-7_8⟩. ⟨hal-02121311⟩
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