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As a representative scenario of multi-stakeholder collaborative manufacturing, the automotive supply chain (ASC) requires frequent cross-organizational data sharing while ensuring secure access control and accountable usage. However, existing Industrial Data Space (IDS) solutions primarily focus on architectural interoperability and governance, lacking executable mechanisms that jointly enforce fine-grained usage control and post-incident responsibility attribution. To bridge this gap, this study proposes a Trusted Industrial Data Space Architecture for the Automotive Supply Chain (TIDS-ASC), and designs a traceable ciphertext-policy attribute-based encryption scheme (TRA-CP-ABE) as its cryptographic enforcement mechanism. The proposed approach is validated through a real-world case study at a leading ASC enterprise, using 1000 access and tracking records generated by 60 users across multiple roles. Experimental results show that TRA-CP-ABE achieves 100% traceability under fingerprint perturbations of 3%, reduces key storage overhead by up to 40%, and maintains efficient encryption and decryption with attribute sets of up to 80. Results demonstrate that TIDS-ASC provides a practical and scalable solution for secure and traceable data sharing in ASC environments. • A trusted industrial data space is proposed for the automotive supply chain (TIDS-ASC), enabling cross-domain data control and behavioral traceability. • A traceable ciphertext-policy attribute-based encryption algorithm (TRA-CP-ABE) is designed by embedding local fingerprinting, achieving pre-usage control and post-usage accountability. • An implicit user key binding mechanism is implemented, enabling responsibility attribution and violation detection without relying on third-party authorities.
Liao et al. (Fri,) studied this question.