ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 20-28.doi: 10.12142/ZTECOM.202102004

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BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing

LIU Junyu, YANG Yongjian, WANG En()   

  1. Jilin University, Changchun 130012, China
  • Received:2021-03-11 Online:2021-06-25 Published:2021-07-27
  • About author:LIU Junyu received his bachelor’s degree in computer science and technology from Jilin University, China in 2019. Currently, he is pursuing for the master’s degree in computer science and technology at Jilin University. His current research interests include mobile crowdsensing and privacy preserving in mobile computing.|YANG Yongjian received his B.E. degree in automatization from Jilin University of Technology, China in 1983, M.E. degree in computer communication from Beijing University of Post and Telecommunications, China in 1991, and Ph.D. in software and theory of computer from Jilin University, China in 2005. He is currently a professor and a Ph.D. supervisor at Jilin University, Director of Key lab under the Ministry of Information Industry, and Standing Director of the Communication Academy. His research interests include network intelligence management, wireless mobile communication and services, and wireless mobile communication.|WANG En (wangen@jlu.edu.cn) received his B.E. degree in software engineering from Jilin University, China in 2011, and his M.E. degree and Ph.D. in computer science and technology from Jilin University in 2013 and 2016. He is currently an associate professor in the Department of Computer Science and Technology, Jilin University. His current research interests include the efficient utilization of network resources, scheduling and drop strategy in terms of buffer-management, energy-efficient communication between human-carried devices, and mobile crowdsensing.


With the emergence of mobile crowdsensing (MCS), merchants can use their mobile devices to collect data that customers are interested in. Now there are many mobile crowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publish and select the right workers to complete the task of some specific locations (for example, taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in order to select the right workers, the platform needs the actual location information of workers and tasks, which poses a risk to the location privacy of workers and tasks. In this paper, we study privacy protection in MCS. The main challenge is to assign the most suitable worker to a task without knowing the task and the actual location of the worker. We propose a bilateral privacy protection framework based on matrix multiplication, which can protect the location privacy between the task and the worker, and keep their relative distance unchanged.

Key words: mobile crowdsensing, task allocation, privacy preserving