A Privacy Preserving Incentive Mechanism for Intelligent Transportation Systems
Yonghao Wu, Changchun Liu, Zihan Xie · 2024
The development of intelligent transportation has provided an efficient support for urban traffic management and better services for users. However, privacy issue affects users' willingness to share their data. This paper proposes an adaptive budget adjustment scheme based on differential privacy, which utilizes a Tree Binary mechanism to handle real-time traffic flow data, addressing the inadequacy of traditional static protection. The scheme triggers an adaptive adjustment through dynamic thresholds, and optimizes the allocation of privacy budgets based on traffic conditions. In experiments, the RMSE and MAE of this scheme outperform those of fixed budget and fully adaptive adjustment schemes, verifying its effectiveness in privacy protection and data utility.