CTPP: A Conditional Trajectory Privacy Preservation Scheme Using Blockchain in V2X

In Vehicle-to-Everything (V2X), the periodic broadcasting and reception of safety beacons by vehicles are pivotal for ensuring security. Nevertheless, the transmission of these messages in unencrypted formats over public channels presents substantial security risks. Adversaries with prior knowledge can potentially reconstruct the trajectories of targeted vehicles, thereby compromising sensitive information such as the behavioral patterns of drivers. To combat this issue, this paper proposes a novel blockchain-based Conditional Trajectory Privacy Preservation (CTPP) scheme, which consists of two primary components: firstly, an attack-and-defense model is designed where a message blurring technique is utilized to resist the correlational background knowledge attack; secondly, the blockchain is introduced to enable the decentralized storage of beacon messages, thereby facilitating conditional trajectory privacy preservation. Additionally, we develop two novel metrics, namely, the utility loss and the privacy loss, to evaluate the effectiveness of our CTPP. The former is quantified as the disparity between datasets pre and post the application of the privacy preservation mechanism. The latter is calculated as the proportion of the longest common sub-trajectory length to the original trajectory length. Extensive experimental results demonstrate that our CTPP significantly enhances trajectory privacy while incurring a moderate utility loss compared to the existing methods.