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

CCSS：协作研究：迈向保护隐私的移动人群感知：多阶段解决方案

• 批准号: 1949639
• 负责人: Linke Guo
• 金额: $ 2.79万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-14 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949639&HistoricalAwards=false
• 关键词: CCSS; Collaborative; Research; Towards; Privacy

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.

包括智能手机和平板电脑的移动的设备如今正变得非常流行。移动的设备配备了从GPS到摄像头的各种传感器，并与其所有者的固有移动性相结合，能够获取周围环境的丰富信息。然而，移动的人群感知的广泛采用在很大程度上受到其隐私问题的阻碍。为了促进移动的人群感测的每个阶段的功能，包括感测任务分配、感测数据收集和结果聚合，感测设备向服务器报告其位置信息、感测能力、任务偏好和感测结果，服务器将潜在地公开其日常路线、行为模式甚至身份。有了这些顾虑，这个项目的总体目标是解决隐私泄漏问题，从不同阶段的移动的人群感知。隐私增强的移动的人群感知将吸引更多的参与者，从而加速智能医疗，环境监测，交通监控，社会事件观察等的成熟，此外，该项目还将作为教育未来决策者和劳动力的理论和工具的培训基地。研究人员计划为移动的人群感知的不同阶段制定有效和高效的隐私保护计划。它对应于三个紧密交织的研究重点。Thrust I探索保护用户的敏感信息，如位置，传感能力和任务偏好，从服务器，同时仍然允许它最佳或近似地解决任务分配问题。隐私保护方案将基于分解方法和分布式计算算法，而不是基于高度计算密集型的密码技术。Thrust II旨在在数据收集阶段提供用户的位置隐私。由于在特定地理区域内对相同事件执行感测的用户的位置是高度相关的，因此其恶化了单独实现的用户隐私。为了解决这个问题，将通过探索用户之间的合作来开发隐私保护方案。游戏理论将被用来进一步分析用户的策略和互动。Thrust III的目标是在数据分析阶段保护用户的感知数据隐私。该研究的特点是共同考虑由移动的设备的有限传感能力甚至懒惰/恶意用户的不当行为造成的数据不完整性。为了同时实现数据隐私和服务准确性，将结合有效的矩阵完成方法和先进的密码技术开发新的方案。

项目成果

AuthCTC: Defending Against Waveform Emulation Attack in Heterogeneous IoT Environments

• DOI: 10.1145/3320269.3384726
• 发表时间: 2020-10
• 期刊: Proceedings of the 15th ACM Asia Conference on Computer and Communications Security
• 作者: Sihan Yu;Xiaonan Zhang;Pei Huang;Linke Guo;Long Cheng;Kuang-Ching Wang

A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems

• DOI: 10.1109/tkde.2019.2936565
• 发表时间: 2021-03-01
• 期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
• 影响因子: 8.9
• 作者: Zhou, Pan;Wang, Kehao;Zheng, Bolong
• 通讯作者: Zheng, Bolong

Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations

• DOI: 10.1109/tmc.2019.2946800
• 发表时间: 2021-02
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Pei Huang;Xiaonan Zhang;Linke Guo;Ming Li

Hide and Seek: Waveform Emulation Attack and Defense in Cross-Technology Communication

• DOI: 10.1109/icdcs.2019.00114
• 发表时间: 2019-07
• 期刊: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Xiaonan Zhang;Pei Huang;Linke Guo;Yuguang Fang