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

Welcome to the website of our research project: “Towards Privacy-Preserving Mobile Crowd Sensing: A Multi-Stage Solution”. This project is a collaborative effort between two institutions: University of Nevada, Reno, and Binghamton University, State University of New York. This website is created and maintained to disseminate and share research results and other information related to the project.

Project Description

The number of traffic fatalities continues to climb at a high rate. Among them, more than half are vulnerable road users (VRUs). Ensuring VRU safety is an urgent issue as it is essential to allow pedestrians, bicyclists, wheelchair users, and others the safe use of roadways in urban and rural environments around the world. Tremendous efforts have also been devoted to the development of collision avoidance systems (CAS) and advanced driver assistance systems (ADAS). In these solutions, VRUs play a “passive” role in protecting themselves, as they rely on vehicles to detect their presence. Alternatively, our approaches enhance VRU safety in a more “proactive” manner. We have been pursuing to build affordable and intelligent assistive tools to protect VRU safety as they travel in the transportation network.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.

Figure: General architecture of the mobile crowd sensing network.

Publications

Privacy-Preserving Data Aggregation for Mobile Crowdsensing With Externality: An Auction Approach ,
Mengyuan Zhang, Lei Yang, Shibo He, Ming Li, Junshan Zhang,
IEEE Transactions on Networking (ToN), 2021.
Collusion-Resistant Worker Recruitment in Crowdsourcing Systems ,
Mingyan Xiao, Wenqiang Jin, Ming Li, Lei Yang, Arun Thapa, and Pan Li,
IEEE Transactions on Mobile Computing (TMC), 2021.
ULPT: A User-Centric Location Privacy Trading Framework for Mobile Crowd Sensing ,
Wenqiang Jin, Mingyan Xiao, Linke Guo, Lei Yang, and Ming Li,
IEEE Transactions on Mobile Computing (TMC), 2021.
Incentivizing Crowdsensing-based Noise Monitoring with Differentially-Private Locations ,
Pei Huang, Xiaonan Zhang, Linke Guo, and Ming Li,
IEEE Transactions on Mobile Computing (TMC), 2021.
Data-Driven Spectrum Trading with Secondary Users' Differential Privacy Preservation ,
Jingyi Wang, Qixun Zhang, Ming Li, Yuanxiong Guo, Zhiyong Feng, and Miao Pan,
IEEE Transactions on Dependable and Secure Computing (TDSC), 2019.
If You Do Not Care About It, Sell It: Trading Location Privacy in Mobile Crowd Sensing ,
Wenqiang Jin, Mingyan Xiao, Ming Li, and Linke Guo,
Proceedings of IEEE International Conference on Computer Communications (INFOCOM'19).
Securing Task Allocation in Mobile Crowd Sensing: An Incentive Design Approach,
Mingyan Xiao, Ming Li, Linke Guo, Miao Pan, Zhu Han and Pan Li,
Proceedings of IEEE Conference on Communications and Network Security (CNS'19).
DPDA: A Differentially Private Double Auction Scheme for Mobile Crowd Sensing,
Wenqiang Jin, Ming Li, Linke Guo, and Lei Yang,
IEEE Conference on Communications and Network Security (CNS'18).
Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing
Lei Yang, Mengyuan Zhang, Shibo He, Ming Li, and Junshan Zhang,
ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'18).
Motivating Human-Enabled Mobile Participation for Data Ofﬂoading,
Xiaonan Zhang, Linke Guo, Ming Li, and Yuguang Fang,
IEEE Transactions on Mobile Computing (TMC), Vol. 17, No. 7, pp. 1624-1637, July 2018.
Energy-efficient Autonomic Offloading in Mobile Edge Computing,
Changqing Luo, Sergio Salinas, Ming Li, and Pan Li,
IEEE Digital Avionics Systems Conference (DASC'17).