Data collaboration between municipal authorities (MA) and mobility providers (MPs) has brought tremendous benefits to transportation systems in the era of big data. Engaging in collaboration can improve the service operations (e.g., reduced delay) of these data owners, however, it can also raise privacy concerns and discourage data-sharing willingness. Specifically, data owners may be concerned that the shared data may leak sensitive information about their customers' mobility patterns or business secrets, resulting in the failure of collaboration. This paper investigates how privacy-preserving mechanisms can foster data collaboration in such settings. We propose a game-theoretic framework to investigate data-sharing among transportation stakeholders, especially considering perturbation-based privacy-preserving mechanisms. Numerical studies demonstrate that lower data quality expectations can incentivize voluntary data sharing, improving transport-related welfare for both MAs and MPs. Our findings provide actionable insights for policymakers and system designers on how privacy-preserving technologies can help bridge data silos and promote collaborative, privacy-aware transportation systems.

翻译：在大数据时代，市政当局（MA）与出行服务提供商（MP）之间的数据协作为交通系统带来了巨大效益。参与协作能够改善这些数据所有者的服务运营（例如减少延误），但也可能引发隐私担忧并削弱数据共享意愿。具体而言，数据所有者可能担心共享的数据会泄露其客户出行模式或商业机密等敏感信息，从而导致协作失败。本文研究了在此类场景中隐私保护机制如何促进数据协作。我们提出了一个博弈论框架来研究交通利益相关者之间的数据共享，特别考虑了基于扰动的隐私保护机制。数值研究表明，较低的数据质量预期能够激励自愿数据共享，从而提升市政当局和出行服务提供商在交通相关的福利。我们的研究结果为政策制定者和系统设计者提供了可行见解，说明了隐私保护技术如何有助于打破数据孤岛，并推动建立协作性、具备隐私意识的交通系统。