Privacy-Preserving Calibration for Participatory Sensing

By leveraging sensors embedded in mobile devices, participatory sensing tries to create cost-effective, large-scale sensing systems. As these sensors are heterogeneous and low-cost, regular calibration is needed in order to obtain meaningful data. Due to the large scale, on-the-fly calibration utilizing stationary reference stations is preferred. As calibration can only be performed in proximity of such stations, uncalibrated measurements might be uploaded at any point in time. From the data quality perspective, it is desirable to apply backward calibration for already uploaded values as soon as the device gets calibrated. To protect the user’s privacy, the server should not be able to link all user measurements. In this paper, we therefore present a privacy-preserving calibration mechanism that enables both forward and backward calibration. The latter is achieved by transferring calibration parameters to already uploaded measurements without revealing the connection between the individual measurements. We demonstrate the feasibility of our approach by means of simulation.