Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users' mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces.
Minho Shin, Cory Cornelius, Apu Kapadia, Nikos Triandopoulos, and David Kotz. Location Privacy for Mobile Crowd Sensing through Population Mapping. In Sensors, June 2015. 10.3390/s150715285