| Title | A Vehicle Trajectory Adversary Model Based on VLPR Data |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Xiong, Chen, Chen, Hua, Cai, Ming, Gao, Jing |
| Conference Name | 2019 5th International Conference on Transportation Information and Safety (ICTIS) |
| Keywords | adversary model, Adversary Modeling, Adversary Models, closest time method, Data models, data privacy, desensitized VLPR data, Foshan City VLPR data, Human Behavior, image recognition, ITS, Licenses, Metrics, privacy, privacy attacking method, privacy disclosure, privacy protection, pubcrawl, qualified identifier attributes, Resiliency, road average speed, road traffic, road vehicles, Roads, Scalability, target trajectory, traffic engineering computing, Trajectory, trajectory linking, vehicle information, vehicle license plate recognition data, vehicle suspicion list, vehicle trajectory adversary model, VLPR system |
| Abstract | Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future. |
| DOI | 10.1109/ICTIS.2019.8883734 |
| Citation Key | xiong_vehicle_2019 |
A Vehicle Trajectory Adversary Model Based on VLPR Data