The paper is a literature review that classifies privacy-preserving AI techniques in dataspaces using a qualitative taxonomy of privacy, performance, and compliance ratings.
Authentication and authorization in Data Spaces: A relationship-based access control approach for policy specification based on ODRL
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abstract
Data has become a crucial resource in the digital economy, fostering initiatives for secure and sovereign data sharing frameworks such as Data Spaces. However, these distributed environments require fine-grained access control mechanisms that balance openness with sovereignty and security. This paper proposes an extension of the Open Digital Rights Language (ODRL) standard, the ODRL Data Spaces (ODS) profile, aimed at supporting authorization and complementing existing authentication mechanisms throughout the data lifecycle. Additionally, a policy execution engine is introduced to translate ODRL policies into executable formats, enabling effective enforcement. The approach is validated through a use case involving OpenFGA, demonstrating its applicability to relationship-based access control scenarios.
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Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
The paper is a literature review that classifies privacy-preserving AI techniques in dataspaces using a qualitative taxonomy of privacy, performance, and compliance ratings.