A Differentially Private Traffic Monitoring Mechanism for Intelligent Transport Systems

This study examines the problem of accuracy of vehicle data collection in urban environments due to increased vehicle density and advances in information technology in Intelligent Transport Systems (ITS), and the resulting privacy risks. Continuous traffic time-series data may be maliciously inferred to provide traffic congestion information at specific spatio-temporal points, threatening urban security and personal privacy. To address this challenge, the study proposes a continuous data privacy-preserving mechanism based on differential privacy preservation combined with geo-partitioning techniques (using latitude and longitude coordinates) for continuous traffic flow time series data. The mechanism aims to ensure the validity and accuracy of the data while preserving privacy, and to maintain the usefulness of the data when analysing traffic congestion.