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Towards Privacy-Preserving Data Dissemination in Crowd-Sensing Middleware Platform

Romain Sommerard 1, 2 Romain Rouvoy 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 - UMR 9189
Abstract : Crowd-sensing, also known as mobile crowdsourcing, is a growing research topic, which consists in engaging end users in the process of gathering physical measurements in the field. While the democratization of such middleware platforms opens the venue for the observation of phenomenon at scale, it may also raise key issues about the privacy of end users involved in the gathering process. In this paper, we therefore report on our preliminary steps towards enabling privacy-preserving data dissemination in crowd-sensing middleware platforms by adopting a decentralized dissemination strategy to fuzz the contributions of end users. To assess such a strategy, we also report in this paper on our initiative to deploy a large-scale middleware platform to emulate and control a crowd of devices. In particular , we claim that an assessment by simulation is not acceptable for such a kind of contribution and that the mobile engineering community requires a more realistic evaluation testbed.
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Submitted on : Friday, June 17, 2016 - 12:09:51 PM
Last modification on : Tuesday, December 6, 2022 - 12:42:13 PM


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  • HAL Id : hal-01332588, version 1


Romain Sommerard, Romain Rouvoy. Towards Privacy-Preserving Data Dissemination in Crowd-Sensing Middleware Platform. 11èmes journées francophones Mobilité et Ubiquité (UbiMob'16), Jul 2016, Lorient, France. pp.6. ⟨hal-01332588⟩



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