Privacy-Preserving Traffic Monitoring with False Report Filtering via Fog-Assisted Vehicular Crowdsensing

Abstract

Traffic monitoring system empowers cloud server and drivers to collect real-time driving information and acquire traffic conditions. However, drivers are more interested in local traffic, and sending driving reports to a faraway cloud server wastes a lot of bandwidth and incurs a long response delay. Recently, fog computing is introduced to provide location-sensitive and latency-aware local data management in vehicular crowdsensing, but it incurs new privacy concerns since drivers’ information could be disclosed. Although these messages are encrypted before transmission, malicious drivers can upload false reports to sabotage the systems, and filtering out false encrypted reports remains a challenging issue. To address the problems, we define a new security model and propose a privacy preserving traffic monitoring scheme. Specifically, we utilize short group signature to authenticate drivers in a conditionally anonymous way, adopt a range query technique to acquire driving information in a privacy-preserving way, and integrate it to the construction of a weighted proximity graph at each fog node through a WiFi challenge handshake to filter out false reports. Moreover, we use variant Bloom filters to achieve fast traffic conditions storage and retrieval. Finally, we prove security and privacy, evaluate performance with real-world cloud servers.