Collaborative Research: SaTC: CORE: Small: Privacy protection of Vehicles location in Spatial Crowdsourcing under realistic adversarial models

合作研究：SaTC：核心：小：现实对抗模型下空间众包中车辆位置的隐私保护

• 批准号: 2136948
• 负责人: Chenxi Qiu
• 金额: $ 22.71万
• 依托单位: University of North Texas
• 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: 2021-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136948&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

In vehicle-based spatial crowdsourcing (VSC), requesters can outsource their tasks to a group of vehicles, which are required to physically move to tasks' locations to perform services or tasks. To promote a cost-effective task distribution, vehicles need to disclose their location information to VSC servers. Location sharing however raises serious privacy concerns related not only to whereabouts of the vehicles but also to sensitive information such as drivers’ home/working address, sexual preferences, financial status, etc. Current privacy protection mechanisms for location-services include location obfuscation methods according to mobility patterns projected on a 2-dimensional plane, wherein users can move in arbitrary directions without any restriction. Obfuscation algorithms based on a 2-dimensional plane are unable to provide strong privacy guarantees of vehicles whose mobility is restricted by road networks, since road networks and traffic patterns facilitate vehicle tracking and trajectory estimation. This research project aims to develop new location privacy protection techniques by considering vehicles’ realistic mobility features, and consequently lead to a more secure and trustworthy computing environment in VSC. This project paves the way for a more realistic body of work on location privacy, particularly regarding location-based services (LBSs). As privacy concerns are still among the main obstacles for mobile users to participate in many advanced LBSs, this project is poised to contribute to the wider adoption of LBSs for many applications (e.g. location-based recommendation systems). In addition, the project provides a set of diverse and interesting topics for undergraduate and graduate students and outreach activities for the community. The project consists of three tasks. First, the project starts with developing new adversarial models to capture the network-constrained mobility features of multiple vehicles operating over roads. Vehicles’ mobility is described by a Bayesian network, i.e., the exact and the reported locations of vehicles are considered as hidden and observable states, respectively, and the spatial correlation between hidden states can be learned from the road network environment and traffic flow information. Second, as a countermeasure for the adversarial models, the project develops a new location obfuscation paradigm that can effectively protect vehicles' location privacy without compromising quality-of-service (QoS), even assuming that adversaries can leverage vehicles’ mobility features for inference attacks. Since the impact of location obfuscation on both privacy level and QoS vary significantly over different road segments, the new location obfuscation methods are designed to be adaptive to various local road network conditions. Finally, considering the scalability and the dynamics of VSC, the project applies distributed and parallel computing techniques (e.g., optimization decomposition) to guarantee the obfuscation algorithms to be implemented in a time-efficient manner.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.

在基于车辆的空间众包（VSC）中，请求者可以将他们的任务外包给一组车辆，这些车辆需要物理地移动到任务的位置以执行服务或任务。为了促进具有成本效益的任务分配，车辆需要向VSC服务器披露其位置信息。然而，位置共享引起了严重的隐私问题，不仅涉及到车辆的下落，而且还涉及到敏感信息，如驾驶员的家庭/工作地址，性偏好，财务状况等。当前的隐私保护机制的位置服务包括位置混淆方法，根据移动模式投影在一个2维平面上，其中用户可以在任意方向移动，没有任何限制。基于2维平面的模糊算法无法提供其移动性受道路网络限制的车辆的强隐私保证，因为道路网络和交通模式便于车辆跟踪和轨迹估计。本研究计划旨在发展新的位置隐私保护技术，考虑到车辆的现实移动性的特点，从而导致一个更安全和可信的计算环境中的VSC。该项目为更现实的位置隐私工作铺平了道路，特别是关于基于位置的服务（LBS）。由于隐私问题仍然是移动的用户参与许多高级LBS的主要障碍之一，因此该项目有望促进LBS在许多应用（例如基于位置的推荐系统）中的更广泛采用。此外，该项目还为本科生和研究生提供了一系列多样化和有趣的主题，并为社区提供了外展活动。 该项目包括三项任务。首先，该项目从开发新的对抗模型开始，以捕获在道路上运行的多辆车辆的网络约束移动性特征。车辆的移动性由贝叶斯网络描述，即，车辆的精确位置和报告位置分别被认为是隐藏状态和可观察状态，并且隐藏状态之间的空间相关性可以从道路网络环境和交通流信息中学习。其次，作为对抗模型的对策，该项目开发了一种新的位置混淆范式，可以有效地保护车辆的位置隐私，而不会影响服务质量（QoS），即使假设对手可以利用车辆的移动性特征进行推理攻击。由于位置混淆对隐私级别和QoS的影响在不同的路段上有很大的不同，新的位置混淆方法被设计为适应各种当地的道路网络条件。最后，考虑到VSC的可扩展性和动态性，该项目应用了分布式和并行计算技术（例如，该奖项反映了NSF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

CatCharger: Deploying In-Motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic

• DOI: 10.1109/jiot.2021.3121756
• 发表时间: 2022-06
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Li Yan;Haiying Shen;Juanjuan Zhao;Chengzhong Xu;Feng Luo;Chenxi Qiu;Zhe Zhang;Shohaib Mahmud