Research on the Privacy Protection Method Based on Deep Learning
Accurate and real-time trajectory data release can provide users with the latest traffic data information,and help users reasonably plan travel time and route.However,location information with improper releasion and reverse inference can easily disclose users'personal information and even endanger users'life safety.The noise added by differential privacy approach may lead to privacy protections introducing inaccuracies in data publication and validity.In order to improve the accuracy and usability of published data,a data publishing method based on deep learning and differential privacy model is proposed to ensure the safe release of spatiotemporal trajectory data.Firstly,a method of top-down recursive division of regions is designed,meanwhile the privacy budget allocation rules are defined in multiple dimensions according to the increase of recursion depth.Secondly,the temporal and spatial characteristics of the data are extracted by the spatiotemporal graph convolutional network(T-GCN)to predict the privacy budget matrix,and Laplace noise is added to the region to realize the privacy protection of trajectory data.Experimental results show that under the premise of satisfying ε-differential privacy,this method can realize the privacy protection of trajectories more reasonably.
NETL
NSTL
万方数据
维普
