CCSS: Collaborative Research: Towards Privacy-Preserving Mobile Crowd Sensing: A Multi-Stage Solution

如今，包括智能手机和平板电脑在内的移动设备正变得极其普遍。移动设备配备了从GPS到相机的各种传感器，再加上其所有者固有的移动性，能够获取丰富的周围环境信息。然而，移动人群感知的广泛采用在很大程度上受到了隐私问题的阻碍。为了促进移动人群感知每个阶段的功能，包括感知任务分配、感知数据收集和结果聚合，感知设备将他们的位置信息、感知功能、任务偏好和感知结果报告给服务器，服务器可能会披露他们的日常路线、行为模式甚至身份。带着这些担忧，本项目的总体目标是解决移动人群感知不同阶段的隐私泄露问题。隐私增强型移动人群感知将吸引更多参与者，从而加速智能医疗、环境监测、交通监控、社交事件观察等领域的成熟。此外，该项目还将作为未来决策者和工作人员理论和工具教育的训练场。私隐专员计划为流动人群侦测的不同阶段，发展有效和高效率的私隐保护计划。它对应着三个紧密交织在一起的研究推动力。Struts I探索从服务器保护用户的敏感信息，如位置、感知能力和任务偏好，同时仍允许服务器以最佳或近似的方式解决任务分配问题。隐私保护方案将基于分解方法和分布式计算算法来设计，而不是基于高度计算密集型的基于密码的技术。Struts II旨在为用户在数据收集阶段提供位置隐私。由于在特定地理区域内对同一事件进行感知的用户的位置高度相关，这恶化了用户个人获得的隐私。为了解决这个问题，将通过探索用户之间的合作来制定隐私保护计划。将采用博弈论来进一步分析用户的策略和互动。推力III的目标是在数据分析阶段保护用户的感知数据隐私。这项研究的特点是联合考虑移动设备有限的感知能力导致的数据缺陷，甚至懒惰/恶意用户的不当行为。为了同时实现数据隐私和服务的准确性，将结合高效的矩阵补全方法和先进的密码技术开发新的方案。

Mobile devices, including smartphones and tablets, are becoming extremely prevalent nowadays. Equipped with diverse sensors, from GPS to camera, and paired with the inherent mobility of their owners, mobile devices are capable of acquiring rich information of surrounding environment. However, the wide adoption of mobile crowd sensing is largely hindered by its privacy concerns. To facilitate the functionality of each stage of mobile crowd sensing, including sensing task allocation, sensing data collection, and result aggregation, sensing devices report their location information, sensing capabilities, task preferences, and sensing results to servers that will potentially disclose their daily routings, behavior patterns and even identities. With these concerns, the overall goal of this project is to address privacy leakage issues from different stages of mobile crowd sensing. Privacy-enhanced mobile crowd sensing will attract more participants and thus accelerate the maturity of smart health care, environment monitoring, traffic surveillance, social event observation, etc. In addition, this project will also serve as a training ground for educating future decision-makers and workforce on theory and tools. The PIs plan to develop effective and efficient privacy preservation schemes for different stages of mobile crowd sensing. It corresponds to three closely intertwined research thrusts. Thrust I explores protecting user's sensitive information, such as locations, sensing capabilities and task preferences, from the server, while still allowing it to optimally or approximately solve task allocation problems. Rather than highly computationally-intensive crypto-based techniques, privacy preservation schemes will be designed based on decomposition methods and distributed computing algorithms. Thrust II aims to provide user's location privacy in the stage of data collection. Since locations of users, who perform sensing over the same event within a certain geographic area, are highly correlated, it deteriorates user's privacy achieved individually. To address this issue, privacy preservation schemes will be developed by exploring collaborations among users. Game theories will be adopted to further analyze users' strategies and interactions. The objective of Thrust III is to protect users' sensing data privacy during the stage of data analysis. The research is featured by jointly considering the data imperfection that is caused by the limited sensing capabilities at mobile devices and even the misbehavior of lazy/malicious users. To achieve data privacy and service accuracy simultaneously, novel schemes will be developed combining efficient matrix completion methods and advanced crypto techniques.