摘要
With the emergence of mobile crowdsensing (MCS), merchants can use their mobiledevices to collect data that customers are interested in. Now there are many mobilecrowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publishand 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 orderto select the right workers, the platform needs the actual location information of workersand tasks, which poses a risk to the location privacy of workers and tasks. In this paper, westudy privacy protection in MCS. The main challenge is to assign the most suitable worker toa task without knowing the task and the actual location of the worker. We propose a bilateralprivacy protection framework based on matrix multiplication, which can protect the locationprivacy between the task and the worker, and keep their relative distance unchanged.
