Private Rendezvous-based Calibration of Low-Cost Sensors for Participatory Environmental Sensing.

Ever-connected smart phones and advanced sensors have lead to new sensing paradigms that promise environmental monitoring in unprecedented spatio-temporal resolution. Especially in air quality sensing with low-cost sensors, regular in-situ device calibration is a helpful approach to ensure data quality. In participatory sensing scenarios, privacy implications arise, as personal sensor data, time and location need to be exchanged. We present 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 sensor 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.