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

2026-02-02

AI Summary

This study addresses the reluctance of municipal agencies and mobility service providers to share data due to privacy concerns, which hinders collaborative optimization of transportation systems. To overcome this barrier, the authors propose a game-theoretic framework incorporating a perturbation-based privacy-preserving mechanism and analyze its incentive effects on multi-party data-sharing behavior. The findings reveal that moderately lowering expectations regarding data quality can effectively encourage voluntary data sharing, thereby enhancing both operational efficiency and social welfare while preserving privacy. This work offers a novel strategy for balancing privacy and utility, providing theoretical support for the design of privacy-aware collaborative transportation systems and informing related policy-making.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• data collaboration
• privacy concerns
• transportation systems
• data sharing
• digital transportation marketplaces

Innovation

• privacy-preserving mechanisms
• game-theoretic framework
• data collaboration
• perturbation-based privacy
• digital transportation marketplace