标题： 隐私-效用-公平性：车联网交通管理系统的一种平衡方法 显示英文标题

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

摘要： 基于位置的车辆交通管理在保护敏感地理数据的同时保持交通管理的实用性以及区域间的公平性方面面临重大挑战。 现有的最先进解决方案往往无法达到对抗链接攻击和人口偏差所需的保护水平，导致隐私泄露和数据分析中的不平等。 在本文中，我们提出了一种新算法，旨在解决基于位置的车辆交通管理系统中隐私、实用性和公平性之间的平衡问题。 在此背景下，实用性意味着提供可靠且有意义的交通信息，而公平性则确保所有地区和个人在数据使用和决策过程中得到公平对待。 采用差分隐私技术，我们通过集成基于查询的数据访问与迭代混淆和校准噪声注入来增强数据安全性，确保敏感地理数据得到保护。 我们通过实施拉普拉斯机制来确保符合epsilon差分隐私标准。 我们在挪威的基于车辆位置的数据上实现了我们的算法，证明了它在保持交通管理和城市规划的数据实用性的同时，确保所有地理区域得到公平代表，而不会被过度代表或欠代表。 此外，我们根据我们的模型创建了挪威的热力图，展示了各个城市交通状况的隐私化和公平表示。 我们的算法为车辆交通提供了隐私 显示英文摘要

摘要： 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