Location-private traffic flow prediction via an encrypted geometric recruitment framework

Accurate traffic flow prediction at specific road segments is essential for optimizing signal control, mitigating congestion, and improving the efficiency of urban transportation systems. Mobile Crowdsensing (MCS) enables large-scale monitoring by collecting geotagged data from participating vehicles and aggregating them at a centralized server. However, most existing solutions for privacy preservation introduce noise or coarse spatial aggregation of location data, which distorts spatiotemporal patterns and degrades the utility of prediction models, while users remain vulnerable to deanonymization and trajectory re-identification attacks. Approaches based on Differential Privacy (DP) offer formal guarantees by injecting calibrated noise into trajectories or model updates, but this perturbation is particularly harmful for short-horizon traffic flow prediction, where detailed local patterns are crucial. We instead shift from perturbation to encryption-based computation and aim to preserve the utility of the prediction model while still enforcing strong location privacy. We propose a location privacy-preserving traffic flow prediction framework that moves all location-sensitive operations into an encrypted recruitment protocol. A Paillier additively homomorphic encryption scheme supports geometric range queries, in particular, point in rectangle tests for task regions, directly over encrypted coordinates. Service requesters encode task areas as encrypted rectangles, vehicles encrypt their current positions, and edge nodes assist in homomorphic operations. The crowdsensing server can decide whether a vehicle lies inside a task area or satisfies distance constraints without observing raw locations and without modifying the traffic measurements used for learning. On top of this privacy-preserving data acquisition pipeline, we build a Gated Recurrent Unit (GRU) based traffic flow prediction model and evaluate it on real-world data. Because the privacy layer leaves traffic flow values intact, the GRU operates on high-fidelity time series under strict location privacy constraints. Experiments on the PeMS dataset and the NYC Green Taxi Trip Record dataset include recurrent, feed-forward, and representative mainstream spatiotemporal baselines. Models that explicitly capture road-network dependencies achieve lower forecasting errors, while under the evaluated settings, the ablation results show only marginal changes in prediction metrics, because the privacy mechanism is confined to the recruitment stage and does not perturb downstream traffic-flow values. Within this framework, GRU provides a competitive trade-off between predictive accuracy, computational efficiency, and implementation complexity for short-horizon traffic flow forecasting.