A Graph-Based Model for Vehicle-Centric Data Sharing Ecosystem

TL;DR

The problem addressed is privacy and data sharing in modern vehicles, where extensive data collection from multiple sources raises privacy concerns. The authors propose a graph-based model of the vehicle-centric data sharing ecosystem, built using the ontology development 101 methodology and integrating GPT-4 privacy-policy analysis, a small-scale SLR, and the VSSo ontology, formalized as a directed graph $\mathcal{G}= (\mathcal{V},\mathcal{E})$ with entity-type and instance graphs. Two real-world scenarios demonstrate how the model reveals complex data-flow paths among drivers, passengers, vehicles, service providers, and authorities, enabling privacy-risk analysis and governance considerations. The work provides a scalable foundation for automated reasoning, topological privacy-risk analysis, and cross-context comparisons in future transportation contexts.

Abstract

The development of technologies has prompted a paradigm shift in the automotive industry, with an increasing focus on connected services and autonomous driving capabilities. This transformation allows vehicles to collect and share vast amounts of vehicle-specific and personal data. While these technological advancements offer enhanced user experiences, they also raise privacy concerns. To understand the ecosystem of data collection and sharing in modern vehicles, we adopted the ontology 101 methodology to incorporate information extracted from different sources, including analysis of privacy policies using GPT-4, a small-scale systematic literature review, and an existing ontology, to develop a high-level conceptual graph-based model, aiming to get insights into how modern vehicles handle data exchange among different parties. This serves as a foundational model with the flexibility and scalability to further expand for modelling and analysing data sharing practices across diverse contexts. Two realistic examples were developed to demonstrate the usefulness and effectiveness of discovering insights into privacy regarding vehicle-related data sharing. We also recommend several future research directions, such as exploring advanced ontology languages for reasoning tasks, supporting topological analysis for discovering data privacy risks/concerns, and developing useful tools for comparative analysis, to strengthen the understanding of the vehicle-centric data sharing ecosystem.

Figures (3)

• Figure 1: An entity type graph
• Figure 2: An entity-level graph for an Uber booking scenario
• Figure 3: An entity-level graph for a speeding scenario