Modelling and Simulation on Location Privacy Preserving Based on Dynamic Periodic Pseudonym

Abstract

Abstract-The location-aware privacy preserving is the key issue to connected vehicles, since they share many traffic information when driving, which possibly leak their location privacies at the same time. In this paper, we propose a novel method of dynamic periodic pseudonym (DPP) improve the location privacy security without loss of location precision, via breaking the link between vehicle's identity and real-time location. The main idea of DPP is that vehicle can change the pseudonym at appropriate time when the calculated privacy leak probability beyond the predefined threshold, overcoming the weakness of constant period of changing pseudonym. In this paper, the privacy leak probability is modelled as gamma distribution, and convolute with the constant pseudonym period, to achieve the effect of adaptive changing. Finally, the simulation shows the effectiveness of the DPP under the different privacy leak environment.