Adaptive N-Gram Model Based Differentially Private Trajectory Synthesis Method

With the development of data science and deep learning, people tend to share their data to boost the precision of the trained model. However, the data to be shared may contain a very large number of private information, which may harm user privacy. Therefore, in this paper, based on n-gram model, a differentially private trajectory data publishing framework is proposed. To mitigate the impact of noise on the utility, we novelly propose a sparse vector technique based tree pruning method to generate an adaptive n-gram model which can adapt to height-unlimited tree. We also combine n-gram model and start-end distribution to solve the intrinsic problem of existing methods based on Markov model. Finally, we use empirical experiments to verify that our method could synthesize more similar trajectories to the original dataset than the state-of-the-art method.