Mobile Sensors as Traffic Probes: Addressing Transportation Modeling and Privacy Protection in an Integrated Framework
Xuegang Jeff Ban, Marco Gruteser · 2010
Mobile traffic sensor data, once widely available, will significantly enhance current transportation modeling applications such as arterial performance measurement. Receiving and processing mobile sensor data however may involve severe privacy concerns if not properly designed. In contrast to the fact that the current research on transportation modeling and privacy protection is rather separated, we propose in this paper a framework on privacy-aware transportation modeling (PATM) and application-aware privacy protection (AAPP). The proposed framework focuses on the interactions between transportation modeling and privacy preserving, being aware of privacy when developing transportation models as well as application needs when designing privacy preserving mechanisms. Using two case studies, we show how PATM and AAPP may be applied in privacy-preserving mobile data collection while satisfying the application needs at the same time. The paper is concluded by discussing how to design a unified approach to guarantee privacy and data needs for various applications.