在数字交通市场中促进数据协作：隐私保护机制的作用
Fostering Data Collaboration in Digital Transportation Marketplaces: The Role of Privacy-Preserving Mechanisms
AI总结：
本文提出博弈论框架，探讨隐私保护机制如何促进交通领域数据协作，通过降低数据质量期望激励自愿共享，提升运输福利。
摘要：
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。参与协作可以改善这些数据所有者的服务运营（例如减少延误），但同时也可能引发隐私问题并抑制数据共享的意愿。具体而言，数据所有者可能担心共享的数据可能泄露关于其客户出行模式或商业机密的敏感信息，导致协作失败。本文研究了隐私保护机制如何在这样的设置中促进数据协作。我们提出了一种博弈论框架，以研究交通相关方之间的数据共享，特别是考虑基于扰动的隐私保护机制。数值研究显示，较低的数据质量期望可以激励自愿的数据共享，提高与运输相关的福利，使MA和MP均受益。我们的发现为政策制定者和系统设计者提供了可操作的见解，说明隐私保护技术如何帮助消除数据孤岛，推动协作且隐私友好的交通系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
