# A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing

## TL;DR

This paper introduces a privacy-preserving traffic monitoring scheme that aggregates vehicle speeds using cryptographic techniques, ensuring privacy while providing accurate traffic data for congestion management.

## Contribution

The paper presents a novel traffic monitoring scheme that protects vehicle privacy using homomorphic encryption and aggregation, which was not addressed in prior work.

## Key findings

- The scheme effectively preserves vehicle privacy including identities, speeds, and locations.
- Simulations show the scheme is efficient and suitable for real-world deployment.
- The approach maintains accurate average speed data for traffic monitoring.

## Abstract

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.