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Privacy-preserving computation of participatory noise maps in the cloud

dc.contributor.authorDrosatos, George
dc.contributor.authorEfraimidis, Pavlos S.
dc.contributor.authorAthanasiadis, Ioannis N.
dc.contributor.authorStevens, Matthias
dc.contributor.authorD’Hondt, Ellie
dc.description.abstractThis paper presents a privacy-preserving system for participatory sensing, which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, deployed in the cloud, where it collaborates on distributed computations without loss of privacy, including with respect to the cloud service providers. We present a generic system architecture involving a cryptographic protocol based on a homomorphic encryption scheme for aggregating sensing data into maps, and demonstrate security in the Honest-But-Curious model both for the users and the cloud service providers. We validate our system in the context of NoiseTube, a participatory sensing framework for noise pollution, presenting experiments with real and artificially generated data sets, and a demo on a heterogeneous set of commercial cloud providers. To the best of our knowledge our system is the first operational privacy-preserving system for participatory sensing. While our validation pertains to the noise domain, the approach used is applicable in any crowd-sourcing application relying on location-based contributions of citizens where maps are produced by aggregating data – also beyond the domain of environmental monitoring.en_UK
dc.publisherElsevier Ltd.en_UK
dc.relation.ispartofseriesJournal of Systems and Software;vol. 92
dc.rightsCopyright © 2014 Elsevier Inc. All rights reserved.en_UK
dc.subjectPrivacy-preserving computationen_UK
dc.subjectCloud computingen_UK
dc.subjectParticipatory sensingen_UK
dc.titlePrivacy-preserving computation of participatory noise maps in the clouden_UK

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Copyright © 2014 Elsevier Inc. All rights reserved.
Except where otherwise noted, this item's license is described as Copyright © 2014 Elsevier Inc. All rights reserved.