Digital Product Passports are becoming essential for traceability and circular economy objectives, particularly in critical raw material supply chains. In this study, we present the system architecture design and data model developed within the European Ma-DiTraCe project, which leverage self-sovereign identity and verifiable credentials to ensure secure, interoperable and regulation-compliant traceability. Our approach introduces two key differentiators. First, we propose a method for integrating material finger-prints — derived from chemical, isotopic, or spectral analyses — as cryptographically signed verifiable credentials within Digital Product Passport systems. This establishes a strong, tamper-evident link between the physical and digital layers, addressing a critical vulnerability in existing traceability systems where non-conforming or substituted materials can bypass purely digital metadata checks and compromise supply chain integrity. Second, we design a decentralized red flag governance mechanism that enables dynamic, responsive certification management through aggregated non-conformity signals, in contrast to traditional certification systems that rely on static, periodic audits and often fail to detect non-conformities in real time. Built on Self-Sovereign Identity principles, Verifiable Credentials, and an optional blockchain anchoring layer, the proposed architecture addresses two key challenges: ensuring a persistent, tamper-evident physical-to-digital link and enabling adaptive trust management across multi-actor supply chains. These contributions position Digital Product Passports as trusted and adaptive instruments for real-time supply chain compliance.
Building similarity graph...
Analyzing shared references across papers
Loading...
Rouwaida Abdallah
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Doruk Şahinel
TU Dortmund University
Oscar Ansotegui Adarve
IMDEA Nanoscience
Procedia Computer Science
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Université Paris-Saclay
TU Dortmund University
Building similarity graph...
Analyzing shared references across papers
Loading...
Abdallah et al. (Thu,) studied this question.
synapsesocial.com/papers/69c37b54b34aaaeb1a67d996 — DOI: https://doi.org/10.1016/j.procs.2026.02.145