BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing
LIU Junyu, YANG Yongjian, WANG En
(Jilin University, Changchun 130012, China)
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.
mobile crowdsensing; task allocation; privacy preserving