论文标题
保留隐私保存的网络车辆的合作估算，并适用于道路异常检测
Privacy-Preserved Collaborative Estimation for Networked Vehicles with Application to Road Anomaly Detection
论文作者
论文摘要
道路信息（例如道路轮廓和交通密度）已被广泛用于智能车辆系统中，以提高道路安全，骑行舒适性和燃油经济性。但是，车辆的异质性和参数不确定性使单个车辆难以准确可靠地测量此类信息非常困难。在这项工作中，我们提出了一个统一的框架，用于基于学习的协作估算，以融合从连接的异构车辆的机队中进行当地的道路估算。协作估计方案利用了多个横穿相同道路细分市场的车辆进行的顺序测量，并让这些车辆将学习信号传递给迭代的局部估计。鉴于必须通过协作估算来保护单个车辆的身份的隐私，因此我们将隐私保护设计直接纳入协作估算设计中，并建立一个统一的框架，以提供隐私的协作估算。与修补常规隐私机制（如差异隐私）不同，它将损害算法准确性或同型加密，从而引起大量的通信/计算开销，我们利用集体估计的动态性能来实现固有的隐私保护而无需牺牲准确性或显着增加沟通/计算的通信/计算/计算开销。数值模拟证实了我们提出的框架的有效性和效率。
Road information such as road profile and traffic density have been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, vehicle heterogeneity and parameter uncertainty make it extremely difficult for a single vehicle to accurately and reliably measure such information. In this work, we propose a unified framework for learning-based collaborative estimation to fuse local road estimation from a fleet of connected heterogeneous vehicles. The collaborative estimation scheme exploits the sequential measurements made by multiple vehicles traversing the same road segment and let these vehicles relay a learning signal to iteratively refine local estimations. Given that the privacy of individual vehicles' identity must be protected in collaborative estimation, we directly incorporate privacy-protection design into the collaborative estimation design and establish a unified framework for privacy-preserving collaborative estimation. Different from patching conventional privacy mechanisms like differential privacy which will compromise algorithmic accuracy or homomorphic encryption which will incur heavy communication/computational overhead, we leverage the dynamical properties of collective estimation to enable inherent privacy protection without sacrificing accuracy or significantly increasing communication/computation overhead. Numerical simulations confirm the effectiveness and efficiency of our proposed framework.
