Synthetic trajectory data generation offers privacy while maintaining utility, but current solutions lack formal privacy guarantees and face limitations in practical evaluations.
The authors explore the balance between privacy and utility in trajectory generation, proposing a framework for privacy-preserving approaches while highlighting the limitations of existing methods.