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

TL;DR

This paper introduces a novel algorithm that balances privacy, utility, and fairness in vehicular traffic management by employing differential privacy techniques to protect sensitive location data while ensuring equitable regional representation.

Contribution

The paper presents a new differential privacy-based algorithm that simultaneously enhances data security, maintains traffic data utility, and ensures fairness across regions in vehicular traffic systems.

Findings

The algorithm effectively protects geographical data against linkage attacks.

It maintains high utility of traffic information for urban planning.

It ensures fair regional representation in traffic data analysis.

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