Abstract: Aiming at the problem of location privacy leakage of workers in traditional mobile crowd sensing, we proposed a two-stage location privacy protection method. Firstly, a system model combining blockchain and edge computing was designed to replace the third-party platform. Secondly, a ciphertext time worker selection algorithm based on homomorphic encryption was proposed in the task allocation stage, which efficiently completed the task allocation through the cooperation of edge nodes. Finally, in the data upload stage, a two disturbance local differential privacy algorithm was given, workers perturbed location data locally, and the interference factor ω was added to balance the protection strength and the quality  loss. Simulation experimental results show that compared with the existing algorithms, the proposed method improves the task completion rate, reduces the  service quality loss, and effectively protects the location privacy of workers.

Key words: mobile crowd sensing, block chain, edge computing, location privacy, homomorphic encryption, differential privacy