DS-DUC: A method for data usage control in automotive supply chain based on industrial data space

ABSTRACT The automotive supply chain (ASC), a cornerstone of industrial collaboration and innovation, relies heavily on secure and efficient data sharing across stakeholders. However, its inherent data heterogeneity, frequent updates, and multi-party collaboration requirements introduce significant challenges for access and usage control. Existing approaches often neglect the concerns of data providers regarding post-access data misuse, leading to restricted data sharing and operational inefficiencies. To address these challenges, this paper proposes a novel data usage control method tailored for the ASC, referred to as DS-DUC (Data Space-based Usage Control), which aims to enhance both the security and controllability of data sharing within the ASC. Central to this method is an improved Extended Usage Control (EUCON) model, seamlessly integrated into a customized data space architecture (DS-ASC-UC), enables modular separation of control logic and rules, supports dynamic, context-aware access enforcement, and facilitates fine-grained policy specification. The proposed method is validated in high-concurrency ASC scenarios. Results indicate that DS-DUC significantly enhances the trustworthiness and adaptability of data sharing across heterogeneous supply chain entities. An open-source implementation of the EUCON is available at https://github.com/LYQ66666/EUCON .