A Multi Trajectory Privacy Protection Method for IoT Based on Particle Swarm Optimization

The rapid development of Internet of Things technology is changing our daily life and the global industrial pattern at an unprecedented speed. It has brought a revolution of intelligence and personalization to personal consumption. The research on multi trajectory privacy protection has a positive impact on the security of consumer data. This paper focuses on the correlations between multiple trajectories. To streamline trajectory data, the quad-tree method is employed to partition the road network area and segment trajectories into discrete units. Subsequently, we quantify the correlation between the original trajectory and others using visit probability vectors, aiming to reduce their similarity. Within specified constraints, refining visit probability vectors via an optimized particle swarm optimization approach tailored for differential privacy. Experiments conducted on real datasets attest to the solution’s robustness and its ability to achieve a better trade-off between privacy protection and data utility effectively.