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Empowering Mobile Crowdsourcing Apps with User Privacy Control

Lakhdar Meftah 1 Romain Rouvoy 1, 2 Isabelle Chrisment 3
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 RESIST - Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
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 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.
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https://hal.inria.fr/hal-02910246
Contributor : Romain Rouvoy <>
Submitted on : Saturday, August 1, 2020 - 1:05:01 PM
Last modification on : Monday, December 14, 2020 - 5:38:27 PM

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Lakhdar Meftah, Romain Rouvoy, Isabelle Chrisment. Empowering Mobile Crowdsourcing Apps with User Privacy Control. Journal of Parallel and Distributed Computing, Elsevier, 2020, pp.15. ⟨10.1016/j.jpdc.2020.07.011⟩. ⟨hal-02910246⟩

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