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

TL;DR

The paper addresses how privacy-preserving mechanisms can foster data collaboration between Municipal Authorities and Mobility Providers in digital transportation marketplaces. It develops a Stackelberg game framework where a data requester (MA) sets data-requests and data owners (MPs) choose participation and privacy levels via perturbation-based mechanisms, then instantiates the model in a traffic signal optimization problem using DP-LWR demand estimation. Theoretical results establish equilibrium existence and conditions for successful collaboration, while numerical experiments with Hangzhou arterial data show moderate data-quality requirements can incentivize data sharing and improve welfare for all stakeholders. The work provides actionable guidance for policymakers and system designers on balancing privacy and data utility to bridge data silos and enable privacy-aware transportation systems.

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.

Figures (7)

• Figure 1: Illustration of data collaboration without (a) and with (b) privacy-preserving mechanisms in transportation systems. Without privacy-preserving mechanisms (a), data sharing is limited to a binary choice and may result in full privacy leakage. With privacy-preserving mechanisms (b), data owners determine the level of data distortion, balancing privacy leakage and data utility.
• Figure 2: Examples of data-sharing topology. (a) Star-like topology, (b) Fully-connected topology.
• Figure 3: Illustration of the shockwaves on the time-space diagram. The blue dashed lines refer to unobserved vehicle trajectories, while the blue solid lines refer to observed vehicle trajectories. The blue cross-shaped points refer to FoQ trajectory points.
• Figure 4: Illustration of the urban arterial network with three intersections.
• Figure 5: The utility of MP 1 and MP 2 with respect to different privacy parameters $\epsilon_1$ and $\epsilon_2$ chosen by MP 1 and MP 2, respectively.

Theorems & Definitions (34)

• Remark 1: Examples of data-sharing topology
• Definition 1: Data request
• Definition 2: Perturbation-based privacy-preserving mechanism
• Definition 3: Data-sharing policy
• Definition 4: Stackelberg-Nash equilibrium (SNE)
• Definition 5: Two-stage equilibrium seeking process for the follower model
• Proposition 1: Validity of the two-stage process
• Theorem 2: Existence of lower-stage equilibrium
• proof
• Theorem 3: Submodularity of the upper-stage binary game