Privacy Protection Method for Fine-Grained Urban Traffic Modeling Using Mobile Sensors

Privacy in transportation is controversial and under-studied. With the ubiquitous applications of Intelligent Transportation System (ITS) technologies, privacy issues in transportation are becoming increasingly important and need to be addressed carefully. As a well-known trade-off, data needs and privacy protection should be deliberately balanced for different applications. This paper focuses on developing privacy mechanisms to simultaneously satisfy privacy protection and modeling needs for fine-grained urban traffic modeling using mobile sensors. To accomplish this, a virtual trip lines (VTL) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed to evaluate the effectiveness of such system by making privacy attacks. The results show that besides ensuring an acceptable level of privacy, the released datasets from such privacy-enhancing system can also be applied to traffic applications with satisfactory performance. Albeit application specific, such “Privacy-by-Design” approach would hopefully shed some light on other applications.

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