Privacy-preserved traffic forecast scheme for intelligent transportation system

Abstract

Traffic forecast in intelligent transportation system (ITS) involves the responsibility of traffic conductor, traffic control and so on, which is an important part in our daily life. Nowadays, traffic forecast schemes in ITS have been widely studied by researchers in many countries. However, these traffic forecast schemes do not take users' privacy into consideration. Users' privacy information is always leaked to the infrastructures and other users in the same system, and this can cause great damage to the information owner. In this paper, a privacy-preserving traffic forecast scheme is proposed, for ITS, to solve the problem of traffic forecast and privacy leakage together. The proposed scheme is based on a recurrent neural network which is operated by the infrastructures. In addition, the infrastructures or other vehicles can get nothing about the sender's privacy from the data package sent by a target vehicle. Our simulation can prove the advantages of our scheme in terms of the forecast accuracy. The security analysis can prove the privacy-preserving property. At the end of simulation part, we give a detailed analysis of forecast accuracy and the fault tolerance of our traffic forecast scheme.