STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion

Abstract

There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy-preserving vehicle assignment strategy via spatial–temporal fusion (STF-LPPVA). Specifically, the strategy allows the cloud platform to train and distribute a spatial–temporal representation model to the user side. Encoded by this model, drivers and passengers can privately fuze the spatial–temporal information of their trips and then transmit these fuzed vectors to the cloud platform. Based on the similarity of the vectors, the cloud platform can allocate vehicles using the Kuhn–Monkreth (KM) algorithm. In addition, we analyze the theoretical feasibility of the STF-LPPVA strategy using entropy change and get good performance with a dataset from DiDi in Chengdu, China. The results show that the successful matching rate of the STF-LPPVA strategy is very close to the original data matching with lower time overhead. Our approach can reduce the traveling distance by 66.5% and improve the matching success rate by 36.2% on average.