PAPU: Pseudonym Swap With Provable Unlinkability Based on Differential Privacy in VANETs

Abstract

Nowadays, the pseudonym swap has become the mainstream technology for protecting vehicles’ trajectory privacy in vehicle ad hoc networks. However, the existing pseudonym swap methods cannot strictly provide the unlinkability between the new pseudonym and old pseudonym of the vehicle due to the lack of theoretical privacy guarantee, resulting in severe leakages of vehicles’ trajectory privacy. Our experiment also proves this point and we find that existing works may cause vehicle’s pseudonyms to be linked with a probability higher than 60% because they always choose two vehicles with very different driving states (e.g., speeds, directions, and positions) to swap their pseudonyms. To solve this issue, we first give a formal privacy definition based on generalized differential privacy, called pseudonym indistinguishability, to provide a strict unlinkability for pseudonym swap. Then, we design an appropriate utility metric and a new pseudonym swap mechanism, which selects a pseudonym for a vehicle by adapting a differential privacy exponential mechanism to satisfy pseudonym indistinguishability. Abstracting from attackers’ prior knowledge, we can strictly guarantee that if two vehicles have a high similarity of driving states, it is impossible for attackers to link the vehicles and their pseudonyms after the swap. Theoretical analyses prove that our mechanism satisfies the proposed privacy definition, thus ensuring the unlinkability between the new pseudonym and the old pseudonym. Extensive experiments on a real data set show that our work only requires about 50% of pseudonym quantities compared to other works and can make the vehicle successfully complete the swap process with a probability of more than 90%, which is higher than any of existing works.