BiGRU-DP: Improved differential privacy protection method for trajectory data publishing

Ignoring the temporal relationship of position points in trajectory data or the change in the number of position points in dummy trajectory generation can result in inadequate data availability. To address the data availability problem of publishing user trajectory data while ensuring privacy protection, this paper proposes an improved privacy protection method combining a Bidirectional Gated Recurrent Unit and differential privacy (BiGRU-DP). First, Laplace noise is added to the time attribute of the trajectory data. Secondly, BiGRU is used to predict the processing of trajectory data, considering the bi-directional data of the predicted point. Then, the k -means clustering method is used to generalize and optimize the trajectory data. Finally, the trajectory data is published after adding noise. The experimental simulation results show that BiGRU-DP enhances the availability of published data and has certain efficiency advantages over traditional trajectory data publishing methods while ensuring privacy protection.