| Paper | Model Name | Year | Model | Evaluation | Dataset | Code |
|---|---|---|---|---|---|---|
| Berke et al. Generating synthetic mobility data for a realistic population with {RNNs} to improve utility and privacy | - | 2022 | GAN, LSTM, RNN | KL on Distance, Locations per User, Aggregate Time per Location | - | https://github.com/aberke/lbs-data/tree/master/trajectory_synthesis |
| Zhan et al. Privacy-Aware Human Mobility Prediction via Adversarial Networks | LSTM-PAE | 2022 | AE, LSTM | Accuracy, Information Loss in Recostruction Process, User-re Identification Inaccuracy | bit.ly/Geolife bit.ly/MDC-2 bit.ly/Foursquare-Data | - |
| Feng et al. Learning to Simulate Human Mobility | MoveSim | 2020 | GAN, self-attention, CNN | Distance, rg, p(r,d), DailyLoc, G-rank, I-rank | bit.ly/Geolife | bit.ly/MoveSim |
| Huang et al. A Variational Autoencoder Based Generative Model of Urban Human Mobility | SVAE | 2019 | VAE, LSTM | MDE | - | - |
| Ouyang et al. A Non-Parametric Generative Model for Human Trajectories | Ouyang GAN | 2018 | WGAN, CNN | bit.ly/MDC-2 | - | |
| Kulkarni et al. Generative models for simulating mobility trajectories | - | 2018 | RNN, GAN, copula | Statistical similarity, privacy test | bit.ly/MDC-2 | - |
| Yin et al. GANs based density distribution privacy-preservation on mobility data | - | 2018 | GAN, FC | Reconstruction error, Utility loss | bit.ly/TaxiSF | - |
| Liu et al. trajGANs: Using generative adversarial networks for geo-privacy protection of trajectory data (Vision paper) | trajGAN | 2018 | GANs |