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

合作研究：SaTC：核心：小型：私下收集和分析用于城市交通建模的 V2X 数据

• 批准号: 2034870
• 负责人: Yuan Hong
• 金额: $ 29.99万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034870&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

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.

当被广泛部署时，车联网车辆中的V2X通信可以产生非常大规模和有价值的数据集，这些数据集可以用于广泛的交通安全、移动性和其他相关应用。在不久的将来，所有新的轻型车辆都将被要求安装V2X设备，以便收集有益的数据。一个可部署的隐私保护工具包对于私下收集和分析V2X数据至关重要，这样设想的应用程序才能发挥全部功能。该项目旨在解决城市交通建模中实际V2X数据收集和分析中的此类隐私问题，从而促进互联车辆和V2X系统/应用的实际部署。此外，该项目将研究成果整合到伊利诺伊理工学院和华盛顿大学的课程中，并为研究生和本科生，特别是代表性不足和少数民族学生提供参与前沿研究的机会。它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。该项目开发了一系列新颖的隐私保护V2X数据收集和分析技术，具有可证明的隐私保证。在第一个研究重点中，将开发新颖的V2X数据收集方案，以局部干扰每辆车的V2X数据，并将其聚合用于大规模城市交通建模，同时满足新兴的严格的本地差分隐私（LDP）概念。在第二个研究重点中，将为基础设施和车辆设计基于安全多方计算（MPC）理论的新型加密协议，以安全地分析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其隐私保障，并在仿真平台上实验评估其系统性能，并将其部署在现实世界的互联汽车测试平台上。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Privacy preserving divisible double auction with a hybridized TEE-blockchain system

• DOI: 10.1186/s42400-021-00100-x
• 发表时间: 2021-12
• 期刊: Cybersecurity
• 影响因子: 3.1
• 作者: Bingyu Liu;Shangyu Xie;Yuanzhou Yang;Rujia Wang;Yuan Hong

A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing (Extended Abstract)

数据外包中保护隐私和实用性的通用框架（扩展摘要）

• DOI: 10.1109/icde53745.2022.00151
• 发表时间: 2022
• 期刊: In Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE'22
• 作者: Xie, Shangyu;Mohammady, Meisam;Wang, Han;Wang, Lingyu;Vaidya, Jaideep;Hong, Yuan
• 通讯作者: Hong, Yuan

OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization

• DOI: 10.14778/3565816.3565823
• 发表时间: 2022-10
• 期刊: Proc. VLDB Endow.
• 作者: Xiaochen Li;Yuke Hu;Weiran Liu;Hanwen Feng;Li Peng;Yuan Hong;Kui Ren;Zhan Qin

Infrastructure-Enabled GPS Spoofing Detection and Correction

• DOI: 10.1109/tits.2023.3298785
• 发表时间: 2022-02
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Feilong Wang;Yuan Hong;X. Ban

UniCR: Universally Approximated Certified Robustness via Randomized Smoothing

• DOI: 10.48550/arxiv.2207.02152
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: Hanbin Hong;Binghui Wang;Yuan Hong