Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing
Journal Title: EAI Endorsed Transactions on Internet of Things - Year 2017, Vol 3, Issue 10
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.
Authors and Affiliations
Jan-Frederic Markert, Matthias Budde, Gregor Schindler, Markus Klug, Michael Beigl
Keywords
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- EP ID EP46498
- DOI http://dx.doi.org/10.4108/eai.15-1-2018.153564
- Views 298
- Downloads 0
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