Collaborative Research: SaTC: CORE: Small: Privately Collecting and Analyzing V2X Data for Urban Traffic Modeling

在广泛部署后，互联车辆中的车辆到一切(V2X)通信可以产生非常大规模且有价值的数据集，这些数据集可用于广泛的交通安全、移动性和其他相关应用。正在提议所有新的轻型车在不久的将来安装V2X设备，以进行这种有益的数据收集。对于私密收集和分析V2X数据，迫切需要一个可部署的隐私保护工具包，以使设想的应用程序能够完全发挥作用。该项目旨在解决用于城市交通建模的实际V2X数据收集和分析中的此类隐私问题，从而将促进互联车辆和V2X系统/应用的真实部署。此外，该项目将研究成果纳入伊利诺伊理工学院和华盛顿大学的课程，并为研究生和本科生，特别是代表不足的学生和少数族裔学生，提供参与尖端研究的机会。该项目还将最先进的隐私保护技术传播到智能交通和互联汽车社区。本项目开发了一系列具有可证明隐私保障的新型隐私保护V2X数据收集和分析技术。在第一个研究方向中，将开发新的V2X数据收集方案，以由每辆车对V2X数据进行本地扰动，并将这些数据聚合在一起用于大规模城市交通建模，同时满足新出现的严格的局部差异隐私(LDP)概念。在第二个研究方向中，将为基础设施和车辆设计安全多方计算(MPC)理论下的新型密码协议，以安全地分析用于小规模城市交通建模的V2X数据。这两类隐私保护技术有望从根本上推进LDP和MPC的文献(例如，为LDP设计新的随机化机制)。研究团队将从理论上证明他们的隐私保障，并在仿真平台上对他们的系统性能进行实验评估，并将他们部署在真实世界互联汽车测试平台上。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

When widely deployed, Vehicle-to-Everything (V2X) communications in connected vehicles can result in very large-scale and valuable datasets that can be useful for a wide range of transportation safety, mobility, and other related applications. Mandates are being proposed for all new light vehicles to install V2X devices in the near future for such beneficial data collection. A deployable privacy preserving toolkit is critically needed for privately collecting and analyzing V2X data so that the envisioned applications can be fully functional. This project aims at addressing such privacy concerns in practical V2X data collection and analysis for urban traffic modeling, and thus will facilitate the real-world deployment of connected vehicles and V2X systems/applications. Furthermore, this project integrates research results into the curricula at Illinois Institute of Technology, and University of Washington, and provides opportunities for graduate and undergraduate students, especially under-represented and minority students, to participate in cutting-edge research. It also disseminates state-of-the-art privacy preserving techniques into the intelligent transportation and connected vehicles communities.This project develop a series of novel privacy preserving V2X data collection and analysis techniques with provable privacy guarantees. In the first research thrust, novel V2X data collection schemes will be developed to locally perturb V2X data by each vehicle and they will be aggregated for large-scale urban traffic modeling while satisfying the emerging rigorous notion of local differential privacy (LDP). In the second research thrust, novel cryptographic protocols under the secure multiparty computation (MPC) theory will be designed for the infrastructure and vehicles to securely analyze the V2X data for small-scale urban traffic modeling. Such two categories of privacy preserving techniques are expected to fundamentally advance the literature of LDP and MPC (e.g., designing new randomization mechanisms for LDP). The research team will theoretically prove the privacy guarantees for them, and experimentally evaluate their system performance on emulation platforms, as well as deploy them in real-world connected vehicles testbeds.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.