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: 2029976
• 负责人: Dongwon Lee
• 金额: $ 27.28万
• 依托单位: Pennsylvania State Univ University Park
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029976&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，请求可以将其任务外包给一组车辆，这是从物理移动到任务的位置以执行服务或任务所必需的。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。但是，位置共享不仅引起了严重的隐私问题，不仅与车辆的下落有关，而且还与敏感信息有关，例如驾驶员的家庭/工作地址，性偏好，财务状况等。当前的位置服务的隐私保护机制包括位置混淆方法，包括在不带任何仲裁的仲裁范围内提出的次要飞机上预测的依据的移动性模式。基于二维平面的混淆算法无法提供由道路网络限制的车辆的强大隐私保证，因为道路网络和交通模式有助于车辆跟踪和轨迹估计。该研究项目旨在通过考虑车辆的现实移动性功能来开发新的位置隐私保护技术，从而在VSC中带来更安全和值得信赖的计算环境。该项目为在位置隐私方面的更现实的工作铺平了道路，尤其是基于位置的服务（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，因此该项目被毒死，可以为许多应用程序（例如基于位置的推荐系统）广泛采用LBS。此外，该项目为本科生和研究生提供了一系列潜水员和有趣的主题，并为社区提供了外展活动。该项目由三个任务组成。首先，该项目始于开发新的对抗模型，以捕获在道路上运行的多辆车的网络约束移动性功能。车辆的移动性由贝叶斯网络描述，即，车辆的确切位置和报告的位置分别被视为隐藏和可观察的状态，并且可以从道路网络环境和交通流量信息中学到隐藏状态之间的空间相关性。其次，作为对抗模型的对策，该项目开发了一个新的位置混淆范式，可以有效地保护车辆的位置隐私而不会损害服务质量（QOS），甚至假设对手可以利用车辆的移动性用于推理攻击。由于位置混淆对隐私级别和QoS的影响在不同的道路细分市场上差异很大，因此新的位置混淆方法旨在适应各种当地的道路网络条件。 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 honestly of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.