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

2024-09-24

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)

AI Summary

Modern connected and autonomous vehicles (CAVs) extensively collect and share vehicle telemetry and personal data, raising severe privacy risks. Method: This paper introduces the first scalable, vehicle-centric conceptual graph model for data sharing, integrating GPT-4–driven semantic parsing of privacy policies, systematic literature review, ontology engineering, and topological analysis to formally represent multi-stakeholder data flows, accountability relationships, and privacy risk propagation paths among vehicles, users, and service providers. The model supports cross-scenario modeling and identifies structural privacy blind spots in data-sharing ecosystems. Contribution/Results: We deliver a high-fidelity conceptual graph model validated through two real-world case studies. The framework provides a rigorous theoretical foundation and methodological toolkit for regulatory compliance assessment, automotive OEMs’ data governance practices, and the development of privacy-enhancing technologies (PETs).

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Application Domains: Mobility, Driving & Flight

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Modeling vehicle data sharing ecosystem using graph-based approach
• Addressing privacy concerns in connected vehicle data exchange
• Developing scalable framework for analyzing data sharing practices

Innovation

• Ontology 101 methodology for data integration
• Graph-based model for vehicle data sharing
• GPT-4 analysis of privacy policies