Fostering Data Collaboration in Digital Transportation Marketplaces: The Role of Privacy-Preserving Mechanisms

TL;DR

This paper explores how privacy-preserving mechanisms can encourage data sharing between transportation authorities and providers, enhancing system efficiency while safeguarding sensitive information.

Contribution

It introduces a game-theoretic framework analyzing privacy-preserving data sharing, highlighting incentives and impacts on transportation collaboration.

Findings

Lower data quality expectations incentivize sharing

Privacy mechanisms can improve transportation welfare

Policy insights for privacy-aware data collaboration

Abstract

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.