Real-time trajectory privacy protection based on improved differential privacy method and deep learning model

Abstract

Accurate and real-time trajectory data publishing plays an important role in providing users with the latest traffic and road condition information to help in rationally planning travel time and routes. However, the improper publishing of location information and reverse analysis and reasoning can easily leak users’ personal information, which may threaten users’ privacy and lives. Owing to the inclusion of differential privacy model noise, privacy protection introduces inaccuracies in data publishing and validity. To improve the accuracy and usability of published data, we propose a data publishing method based on deep learning and differential privacy models for securing spatiotemporal trajectory data publishing. The method divides the trajectory data into two-dimensional grid regions, counts the density of trajectories at grids, performs a top-down recursive division of regions, and formulates rules for privacy budget allocation from multiple perspectives as recurrence depth increases. Furthermore, the method integrates spatiotemporal sequence data according to temporal order. Subsequently, it extracts temporal and spatial features of the data by the temporal graph convolutional network model for budget matrix prediction, adds Laplace noise to the regions, and evaluates the effect of differential privacy protection with the original data to protect trajectory data privacy. Experiments demonstrate that under the premise of satisfying ε-difference privacy, the query error and Jensen–Shannon divergence are smaller, the Kendall coefficient is more consistent, and the upper and lower limit values are more stable. Hence, the top-down division method achieves better results than those of the two traditional region division methods of the uniform grid and adaptive grid. The proposed method can be used to allocate the privacy budget more reasonably and achieve privacy protection of trajectories, which can be applied to a large amount of spatiotemporal trajectory data.