International Conference · 2023
DEMO: STM - A Privacy-Enhanced Solution for Spatio-Temporal Trajectory Management
Abstract
In this demonstration paper, we present STM: a new system for securing and management of vehicle trajectory data using a generative model that balances privacy and utility. For instance, traditional methods for taxi-demand prediction pose the risk of privacy breaches from both the data and the model. To address this challenge, we deploy Spatiotemporal-GAN to generate synthetic trajectories that meet privacy regulations such as GDPR. We assess the quality of the generated data by constructing several taxi-demand prediction models. Moreover, we evaluate the privacy risk by implementing trajectory user linking attacks against the generated data and membership inference attacks against the prediction model. Our system is designed with rich interactivity and visualization, enabling the audience to use these modules. Overall, our approach demonstrates the potential of generative models in preserving privacy while maintaining data utility in the context of taxi-demand prediction.
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