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In this vision paper, we propose the trajGANs framework and address the potential of using generative adversarial networks for geo-privacy protection of trajectory data. Our goal is to provide a geoprivacy protection layer for trajectory data publication and usage by generating synthetic trajectories that can preserve the summary properties of real data and have close-to-real-data performance in analysis tasks. We summarize the trajectory types in geo-privacy protection and the possible data generation scenarios. We also provide validation metrics and address the possible challenges of implementing trajGANs.