LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

TL;DR

LSTM-TrajGAN is a deep learning model that generates synthetic trajectory data to protect user privacy while maintaining data utility for spatial and temporal analysis.

Contribution

It introduces a novel end-to-end deep learning approach with a custom loss function for privacy-preserving trajectory data generation, outperforming traditional geomasking methods.

Findings

Better prevents user re-identification compared to geomasking

Preserves key spatial, temporal, and thematic trajectory features

Balances privacy protection with data utility

Abstract

The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.