A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing
This addresses privacy concerns in vehicular crowdsourcing for traffic management, but it is incremental as it builds on existing cryptographic techniques.
The paper tackles traffic congestion and privacy risks by proposing a privacy-preserving traffic monitoring scheme that aggregates vehicle speeds using cryptographic methods, achieving protection of identities, speeds, locations, and trajectories without disclosing individual data.
The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.