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: 2302689
• 负责人: Yuan Hong
• 金额: $ 29.99万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302689&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，连接车辆中的车辆到所有（V2X）通信可能会导致非常大规模且有价值的数据集，这对于广泛的运输安全，移动性和其他相关应用程序可能有用。正在提出要求所有新轻型车辆在不久的将来安装V2X设备的授权，以收集此类有益的数据。私下收集和分析V2X数据非常需要部署的隐私保护工具包，以便设想的应用程序可以完全运行。该项目旨在解决城市交通建模实用的V2X数据收集和分析中的这种隐私问题，因此将促进连接车辆和V2X系统/应用程序的现实部署。此外，该项目将研究结果纳入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，尤其是代表性不足和少数族裔学生提供机会参加尖端研究。它还将保留最新的隐私技术传播到智能运输和互联车辆社区中。该项目开发了一系列新型隐私，以保存V2X数据收集和分析技术，并提供可证明的隐私保证。在第一个研究的推力中，新型的V2X数据收集方案将通过每辆车开发用于本地扰动V2X数据，并将汇总用于大规模的城市交通建模，同时满足新兴的当地差异隐私（LDP）的严格概念。在第二项研究中，将为基础架构和车辆设计新型的加密协议（MPC）理论，以安全地分析V2X数据以进行小规模的城市交通建模。这两种隐私保存技术有望从根本上推进自然党和MPC的文献（例如，为LDP设计新的随机机制）。从理论上讲，研究团队将为他们提供隐私保证，并在实验中评估其在模拟平台上的系统性能，并将其部署在现实世界中的连接车辆测试台中。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来通过评估来进行支持的人，并被认为是值得的。

项目成果

Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records

• DOI: 10.1007/978-3-031-43418-1_27
• 发表时间: 2023
• 作者: Fereshteh Razmi;Jian Lou;Yuan Hong;Li Xiong

WPES '22: 21st Workshop on Privacy in the Electronic Society

WPES 22：第 21 届电子社会隐私研讨会

• DOI: 10.1145/3548606.3563220
• 发表时间: 2022
• 期刊: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Hong, Yuan;Wang, Lingyu
• 通讯作者: Wang, Lingyu

Poster: Cryptographic Inferences for Video Deep Neural Networks

海报：视频深度神经网络的密码推理

• DOI: 10.1145/3548606.3563543
• 发表时间: 2022
• 期刊: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS
• 作者: Liu, Bingyu;Wang, Rujia;Ba, Zhongjie;Zhou, Shanglin;Ding, Caiwen;Hong, Yuan
• 通讯作者: Hong, Yuan

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

Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks

• DOI: 10.1109/sp54263.2024.00053
• 发表时间: 2023-07
• 期刊: ArXiv
• 作者: Xinyu Zhang;Hanbin Hong;Yuan Hong;Peng Huang;Binghui Wang;Zhongjie Ba;Kui Ren