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

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，这些车辆被要求物理地移动到任务所在的位置来执行服务或任务。为了促进经济高效的任务分配，车辆需要向VSC服务器披露其位置信息。然而，位置共享引发了严重的隐私问题，不仅涉及车辆的下落，还涉及司机的家庭/工作地址、性偏好、经济状况等敏感信息。目前的位置服务隐私保护机制包括根据二维平面上投影的移动模式进行位置混淆的方法，其中用户可以不受任何限制地向任意方向移动。由于道路网络和交通模式为车辆跟踪和轨迹估计提供了便利，基于二维平面的模糊算法无法为受道路网络限制的车辆提供强大的隐私保障。本研究项目旨在通过考虑车辆的真实移动特性来开发新的位置隐私保护技术，从而在VSC中实现更安全可信的计算环境。该项目为更切合实际的关于位置隐私的工作铺平了道路，特别是关于基于位置的服务(LBSS)。由于隐私问题仍然是移动用户参与许多高级LBSS的主要障碍之一，该项目有望为许多应用程序(例如基于位置的推荐系统)更广泛地采用LBSS做出贡献。此外，该项目还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。该项目由三项任务组成。首先，该项目首先开发新的对抗性模型，以捕捉在道路上运行的多辆车辆的网络受限的机动性特征。车辆的机动性用贝叶斯网络来描述，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测状态，并从路网环境和交通流量信息中学习隐藏状态之间的空间相关性。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例，即使假设对手可以利用车辆的移动性特征进行推理攻击，该范例也可以在不影响服务质量的情况下有效地保护车辆的位置隐私。由于位置混淆对隐私水平和服务质量的影响在不同路段上差异很大，因此新的位置混淆方法被设计成能够适应不同的本地道路网条件。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，优化分解)，以保证模糊算法以高效的方式实施。该奖项反映了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。

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.