Bidirectional LSTM-Based Privacy Preserving Method for Trajectory Generation

To ensure the privacy of trajectory data while improving its usability upon release, machine learning algorithms can be applied to process trajectory data, thereby enhancing its usability. Addressing the issue of trajectory data release usability, we propose a trajectory privacy protection scheme that combines Bidirectional Long Short-Term Memory (BILSTM) networks and differential privacy (DP). The scheme prepossess the trajectory data using BILSTM to improve its usability. For the generated trajectory data, the Laplace mechanism in differential privacy is applied to add noise, thereby achieving privacy protection. The generalized trajectory data-set obtained is then released. This scheme ensures good data usability and offers certain efficiency advantages.