Trajectory time prediction and dataset publishing mechanism based on deep learning and differential privacy

In order to solve the problems of inaccurate trajectory time prediction and poor privacy protection of dataset publishing mechanism, the study adds deep learning models into the trajectory time prediction model and designs the SLDeep model. Its performance is compared with LRD, STTM and DeepTTE models for experiments, and the results show that the SLDeep model. The lowest mean absolute error value was 116.357, indicating that it outperformed the other models. The study designed the Travelet publishing mechanism by incorporating differential privacy methods into the publishing mechanism, and compared it with Li's and Hua's publishing mechanisms for experiments. The results show that the mutual information index value of Travelet publishing mechanism is 0.06, which is better than Li's and Hua's publishing mechanisms. The experimental results show that the performance of the trajectory time prediction model incorporating deep learning and the dataset publishing mechanism incorporating differential privacy methods has been greatly improved, which can provide new ideas to obtain a more accurate and all-round trajectory big data management system.