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

Markert, Jan-Frederic and Budde, Matthias and Schindler, Gregor and Klug, Markus and Beigl, Michael (2017) Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing. EAI Endorsed Transactions on Internet of Things, 3 (10): 2. ISSN 2414-1399

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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.

Item Type: Article
Uncontrolled Keywords: Participatory Sensing, Location Privacy, Sensor Calibration, Mobile Sensing, Environmental Monitoring, Calibration Rendezvous, Citizen Science, Air Pollution
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 22 Sep 2020 13:45
Last Modified: 22 Sep 2020 13:45

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