  The use of trajectory data with abundant spatial-temporal information is
pivotal in Intelligent Transport Systems (ITS) and various traffic system
tasks. Location-Based Services (LBS) capitalize on this trajectory data to
offer users personalized services tailored to their location information.
However, this trajectory data contains sensitive information about users'
movement patterns and habits, necessitating confidentiality and protection from
unknown collectors. To address this challenge, privacy-preserving methods like
K-anonymity and Differential Privacy have been proposed to safeguard private
information in the dataset. Despite their effectiveness, these methods can
impact the original features by introducing perturbations or generating
unrealistic trajectory data, leading to suboptimal performance in downstream
tasks. To overcome these limitations, we propose a Federated Variational
AutoEncoder (FedVAE) approach, which effectively generates a new trajectory
dataset while preserving the confidentiality of private information and
retaining the structure of the original features. In addition, FedVAE leverages
Variational AutoEncoder (VAE) to maintain the original feature space and
generate new trajectory data, and incorporates Federated Learning (FL) during
the training stage, ensuring that users' data remains locally stored to protect
their personal information. The results demonstrate its superior performance
compared to other existing methods, affirming FedVAE as a promising solution
for enhancing data privacy and utility in location-based applications.
