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.

翻译：尽管网联车辆（CVs）在提升交通运行效率方面展现出巨大潜力，但其可能对个体出行者造成隐私风险，例如泄露频繁访问地点、路径行为等敏感信息。尽管已有大量文献设计了利用CV信息的各类算法，但关于隐私保护型交通控制的研究仍处于起步阶段。本文旨在填补这一研究空白，提出一种基于CV数据的隐私保护自适应交通信号控制方法。具体而言，我们利用安全多方计算与差分隐私技术，设计了一种隐私保护的CV数据聚合机制。该机制可在所有CV无需暴露私有数据的前提下，计算关键交通参数。基于数据聚合机制获取的交通变量，我们进一步构建了用于自适应信号控制的线性优化模型。该线性规划问题被扩展为随机规划问题，以显式处理差分隐私机制引入的噪声。评估结果表明：线性优化模型在保持控制性能影响微乎其微的前提下实现了隐私保护；与线性规划模型相比，随机规划模型可显著减少残余队列长度，且几乎不增加车辆延误。总体而言，我们的方法证明了在基于CV的交通建模与控制中引入隐私保护机制的可行性，兼顾了效用性与隐私性。