DP-STGAT: Traffic statistics publishing with differential privacy and a spatial-temporal graph attention network

Abstract

With the continuous development of intelligent transportation, an increasing number of smart devices and sensors are being used to record traffic information. Recently, researchers have relied on machine learning and differential privacy to achieve privacy protection during the continuous release of statistics. However, mechanisms based on differential privacy and prediction are limited by two key issues: the lack of attention given to the spatial-temporal correlations among the input data leads to poor prediction accuracy, and unreasonable privacy budget allocations cause reduced data utility. A traffic statistics publication mechanism with differential privacy and a spatial-temporal graph attention network (DP-STGAT) is proposed to address these problems. The key components include an adjacency matrix based on equivalent distance, a multistep prediction model based on an STGAT, and a combination of pre-allocation and adaptive allocation method for privacy budget allocation. These three components are tightly integrated to improve the accuracy of forecasting and solve the problem regarding poorly allocated privacy budgets. We evaluate the proposed mechanism with two real-world datasets and compare it with four representative methods with a w-event privacy guarantee. The experimental results show that the proposed scheme outperforms the existing methods.