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Towards Privacy-sensitive Mobile Crowdsourcing

Lakhdar Meftah 1, 2
2 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 (CRIStAL) - UMR 9189
Abstract : With the widespread adoption of mobile phones, devices are used to track the user's activity and to collect insightful reports from the environment (e.g., air quality, network quality). Most of these collected data are systematically tagged with the user location which may inevitably lead to user privacy leaks by discarding sensitive information a posteriori based on their potential points of interest. This thesis introduces an anonymous data collection library for mobile apps, a software library that improves the user's privacy without compromising the overall quality of the crowdsourced dataset. In particular, we propose a decentralized approach, named FOUGERE, to convey data samples from user devices using peer-to-peer (P2P) communications to third-party servers, thus introducing an a priori data anonymization process that is resilient to location-based attacks. To validate our approach, we propose a testing framework to test this P2P communication library, named PeerFleet. Beyond the identification of P2P-related errors, PeerFleet also helps to tune the discovery protocol settings to optimize the deployment of P2P apps. We validate FOUGERE using 500 emulated devices that replay a mobility dataset and use FOUGERE to collect location data. We evaluate the overhead, the privacy and the utility of FOUGERE. We show that FOUGERE defeats the state-of-the-art location-based privacy attacks with little impact on the quality of the collected data.
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  • HAL Id : tel-02399716, version 2

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Lakhdar Meftah. Towards Privacy-sensitive Mobile Crowdsourcing. Software Engineering [cs.SE]. University of Lille, 2019. English. ⟨tel-02399716v2⟩

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