RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection

Mobile devices furnish users with various services while on the move, but also raise public concerns about trajectory privacy. Unfortunately, traditional privacy protection methods, such as anonymity and generalization, are not secure because they cannot resist attackers with background knowledge. The emergence of differential privacy provides an effective solution to this problem. Still, the existing schemes are almost designed based on the collected aggregate historical data (so-called static trajectory privacy protection), which are not suitable for real-time dynamic trajectory privacy protection of mobile users. Furthermore, due to the complexity and redundancy features of the full trajectory data, the efficiency and accuracy of the privacy protection model are significantly limited by the existing schemes. In this paper, we propose a new differential privacy scheme base on the Recurrent Neural Network for Dynamic trajectory privacy Protection (RNN-DP). We firstly introduce a recurrent neural network model to handle the real-time data effectively instead of the full data. Secondly, we novelty leverage the dynamic velocity attribute to form a quatemion to indicate the status of the users. Moreover, we design a prejudgment mechanism to increase the availability of differential privacy technology. Compared with the current state-of-the-art mechanisms, the experimental results demonstrate that RNN-DP displays excellent performance in privacy protection and data availability for dynamic trajectory data.