  Although Connected Vehicles (CVs) have demonstrated tremendous potential to
enhance traffic operations, they can impose privacy risks on individual
travelers, e.g., leaking sensitive information about their frequently visited
places, routing behavior, etc. Despite the large body of literature that
devises various algorithms to exploit CV information, research on
privacy-preserving traffic control is still in its infancy. In this paper, we
aim to fill this research gap and propose a privacy-preserving adaptive traffic
signal control method using CV data. Specifically, we leverage secure
Multi-Party Computation and differential privacy to devise a privacy-preserving
CV data aggregation mechanism, which can calculate key traffic quantities
without any CVs having to reveal their private data. We further develop a
linear optimization model for adaptive signal control based on the traffic
variables obtained via the data aggregation mechanism. The proposed linear
programming problem is further extended to a stochastic programming problem to
explicitly handle the noises added by the differentially private mechanism.
Evaluation results show that the linear optimization model preserves privacy
with a marginal impact on control performance, and the stochastic programming
model can significantly reduce residual queues compared to the linear
programming model, with almost no increase in vehicle delay. Overall, our
methods demonstrate the feasibility of incorporating privacy-preserving
mechanisms in CV-based traffic modeling and control, which guarantees both
utility and privacy.
