EAI Endorsed Transactions on Internet of Things 18(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={2018},
        month={1},
        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: 2018
    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.