Location protection method for mobile crowd sensing based on local differential privacy preference

In view of the location privacy problem of participants in mobile crowd sensing, this paper proposes a method to protect the location of participants based on local differential privacy preference. First of all, the map is discretized and mapped from two-dimensional space to one-dimensional space by means of MHC, which can guarantee the spatial correlation, and the map is segmented based on the density of participants using genetic algorithm; Then, according to the personal privacy needs of current location, two different local differential privacy perturbation methods, RAPPOR and k-RR, are chosen by participants; Next, the chosen local differential privacy is used to perturb the location of each participant in the region after segmentation, and the perturbed location data are sent to the data collection server to protect the participants’ locations. Finally, the simulation experiments are carried out and show that map density segmentation can reduce the privacy cost, and the method proposed in this paper is superior to the method using k-anonymous and differential privacy and the method using Hilbert and differential privacy in terms of running time and average relative error, and prove that the execution time is lower and the data availability is improved.