LSTM-TrajGAN and similar generative models can be enhanced to provide formal privacy guarantees by incorporating differential privacy (DP) mechanisms directly into the model architecture. One approach could involve integrating DP techniques, such as adding noise to the training process or applying differential private stochastic gradient descent (DP-SGD), to ensure that the generated trajectories adhere to strict privacy standards. By incorporating DP directly into the model training, it would be possible to guarantee formal privacy guarantees for the synthetic trajectory data generated by LSTM-TrajGAN.