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IoT 17(10): e2

Research Article

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

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  • @ARTICLE{10.4108/eai.15-1-2018.153564,
        author={Jan-Frederic Markert and Matthias Budde and Gregor Schindler and Markus Klug and Michael Beigl},
        title={Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={3},
        number={10},
        publisher={EAI},
        journal_a={IOT},
        year={2017},
        month={4},
        keywords={Participatory Sensing, Location Privacy, Sensor Calibration, Mobile Sensing, Environmental Monitoring, Calibration Rendezvous, Citizen Science, Air Pollution},
        doi={10.4108/eai.15-1-2018.153564}
    }
    
  • Jan-Frederic Markert
    Matthias Budde
    Gregor Schindler
    Markus Klug
    Michael Beigl
    Year: 2017
    Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing
    IOT
    EAI
    DOI: 10.4108/eai.15-1-2018.153564
Jan-Frederic Markert1,2, Matthias Budde1,2,*, Gregor Schindler1,2, Markus Klug1,2, Michael Beigl1,2
  • 1: Karlsruhe Institute of Technology (KIT), TECO / Chair for Pervasive Comuting Systems,
  • 2: Vincenz-Prießnitz-Straße, 176131 Karsruhe, Germany
*Contact email: budde@teco.edu

Abstract

The ubiquity of ever-connected smartphones has lead to new sensing paradigms that promise environmental monitoring in unprecedented temporal and spatial resolution. Everyday people may use low-cost sensors to collect environmental data. However, measurement errors increase over time, especially with low-cost air quality sensors. Therefore, regular calibration is important. On a larger scale and in participatory sensing, this needs be done in-situ. Since for this step, personal sensor data, time and location need to be exchanged, privacy implications arise. This paper presents a novel privacy-preserving multi-hop sensor calibration scheme, that combines Private Proximity Testing and an anonymizing MIX network with cross-sensor calibration based on rendezvous. Our evaluation with simulated ozone measurements and real-world taxicab mobility traces shows that our scheme provides privacy protection while maintaining competitive overall data quality in dense participatory sensing networks.

Keywords
Participatory Sensing, Location Privacy, Sensor Calibration, Mobile Sensing, Environmental Monitoring, Calibration Rendezvous, Citizen Science, Air Pollution
Received
2017-01-30
Accepted
2017-03-08
Published
2017-04-14
Publisher
EAI
http://dx.doi.org/10.4108/eai.15-1-2018.153564

Copyright © 2017 J.-F. Markert, M. Budde et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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