Federated TimeGAN for Privacy Preserving Synthetic Trajectory Generation

Mobility datasets are crucial for various applications. However, sharing this data raises privacy concerns due
to the sensitive nature of geolocation information. Synthetic
data generation has recently emerged as a promising solution
to protect geo-privacy of trajectory data. Current approaches
rely on having a large set of authentic trajectories collected
from individual users to train generative networks. However, this
assumption proves impractical in many real-world scenarios due
to the sensitive personal information typically embedded within
trajectories. Our approach leverages federated learning to generate privacy-preserving synthetic trajectories without the need
for centralized data collection. Experimental results demonstrate
that our distributed framework effectively produces synthetic
trajectories with distributions comparable to baseline, offering
a privacy-conscious alternative for geo-privacy protection in
mobility datasets.