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

arXiv: https://arxiv.org/abs/2507.08864 · HTML: https://mlsanity.com/paper/2507.08864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Group Fairness: Independence Revisited](https://mlsanity.com/paper/2101.02968/) (2021)
- [L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response](https://mlsanity.com/paper/2209.15091/) (2022)
- [Demographic Parity: Mitigating Biases in Real-World Data](https://mlsanity.com/paper/2309.17347/) (2023)
- [Referee-Meta-Learning for Fast Adaptation of Locational Fairness](https://mlsanity.com/paper/2402.13379/) (2024)
- [Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility](https://mlsanity.com/paper/2112.08487/) (2021)
- [Resilient Privacy Protection for Location-Based Services through Decentralization](https://mlsanity.com/paper/2001.07583/) (2020)
- [BUDS: Balancing Utility and Differential Privacy by Shuffling](https://mlsanity.com/paper/2006.04125/) (2020)
- [Fairly Private Through Group Tagging and Relation Impact](https://mlsanity.com/paper/2105.07244/) (2021)
- [Analysing Fairness of Privacy-Utility Mobility Models](https://mlsanity.com/paper/2304.06469/) (2023)

## Abstract (arXiv)

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