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: 2029881
• 负责人: Chenxi Qiu
• 金额: $ 22.71万
• 依托单位: Rowan University
• 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: 2021-01-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029881&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，这些车辆需要实际移动到任务的位置来执行服务或任务。为了促进经济有效的任务分配，车辆需要向VSC服务器公开其位置信息。然而，位置共享引发了严重的隐私问题，不仅涉及车辆的位置，还涉及驾驶员的家庭/工作地址、性取向、财务状况等敏感信息。当前位置服务的隐私保护机制包括根据投影在二维平面上的移动模式进行位置混淆的方法，其中用户可以在不受任何限制的情况下向任意方向移动。基于二维平面的模糊算法无法为受路网限制的车辆提供强大的隐私保证，因为路网和交通模式有利于车辆的跟踪和轨迹估计。本研究项目旨在通过考虑车辆的实际移动特性，开发新的位置隐私保护技术，从而在VSC中建立一个更安全、更可信的计算环境。这个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用户参与许多高级lbs的主要障碍之一，因此该项目准备为lbs在许多应用程序（例如基于位置的推荐系统）中的广泛采用做出贡献。此外，该项目为本科生和研究生提供了一套多样化和有趣的主题，并为社区提供了外展活动。该项目包括三个任务。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汽车的网络约束移动特性。车辆的移动性由贝叶斯网络描述，即将车辆的准确位置和报告位置分别视为隐藏状态和可观察状态，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，即使假设对手可以利用车辆的移动性特征进行推理攻击，该范式也可以在不影响服务质量（QoS）的情况下有效保护车辆的位置隐私。由于位置混淆对隐私级别和QoS的影响在不同路段上差异很大，因此设计了新的位置混淆方法以适应不同的局部路网条件。最后，考虑到VSC的可扩展性和动态性，该项目采用分布式和并行计算技术（如优化分解）来保证混淆算法以高效的方式实现。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。