Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System

2025-07-09

AI Summary

Addressing the challenge of simultaneously ensuring geographic privacy, analytical utility, and cross-regional fairness in vehicular network traffic management, this paper proposes a differentially private algorithm integrating query-based access, iterative shuffling, and adaptive noise calibration. Built upon the Laplace mechanism, the method guarantees strict location privacy (ε-differential privacy) while dynamically allocating noise and rebalancing data to mitigate linkage attacks and demographic bias, thereby enhancing regional representational fairness. Experiments on real-world vehicle trajectory data from Norway demonstrate that the generated privacy-preserving heatmaps accurately capture city-scale traffic patterns while ensuring statistically balanced representation across administrative regions. To the best of our knowledge, this work is the first to achieve provably coordinated optimization of privacy, utility, and fairness in traffic management systems.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Location-based vehicular traffic management faces significant challenges in protecting sensitive geographical data while maintaining utility for traffic management and fairness across regions. Existing state-of-the-art solutions often fail to meet the required level of protection against linkage attacks and demographic biases, leading to privacy leakage and inequity in data analysis. In this paper, we propose a novel algorithm designed to address the challenges regarding the balance of privacy, utility, and fairness in location-based vehicular traffic management systems. In this context, utility means providing reliable and meaningful traffic information, while fairness ensures that all regions and individuals are treated equitably in data use and decision-making. Employing differential privacy techniques, we enhance data security by integrating query-based data access with iterative shuffling and calibrated noise injection, ensuring that sensitive geographical data remains protected. We ensure adherence to epsilon-differential privacy standards by implementing the Laplace mechanism. We implemented our algorithm on vehicular location-based data from Norway, demonstrating its ability to maintain data utility for traffic management and urban planning while ensuring fair representation of all geographical areas without being overrepresented or underrepresented. Additionally, we have created a heatmap of Norway based on our model, illustrating the privatized and fair representation of the traffic conditions across various cities. Our algorithm provides privacy in vehicular traffic

Problem

• Balancing privacy, utility, fairness in traffic management systems
• Protecting geographical data from linkage attacks and biases
• Ensuring equitable data representation across regions and individuals

Innovation

• Differential privacy with query-based access
• Laplace mechanism for epsilon-differential privacy
• Iterative shuffling and calibrated noise injection