Towards Secure and Interoperable Data Spaces for 6G: The 6G-DALI Approach

TL;DR

This paper introduces the 6G-DALI data architecture, a federated and secure data sharing framework designed to meet 6G's complex, trust, and interoperability requirements, enabling scalable AI experimentation.

Contribution

It presents a novel federated dataspace architecture for 6G, extending existing data models with features like low-latency edge processing and dynamic trust management.

Findings

Aligns with GAIA-X and IDSA principles

Supports secure, compliant data sharing at scale

Enhances interoperability for 6G networks

Abstract

The next generation of mobile networks, 6G, is expected to enable data-driven services at unprecedented scale and complexity, with stringent requirements for trust, interoperability, and automation. Central to this vision is the ability to create, manage, and share high-quality datasets across distributed and heterogeneous environments. This paper presents the data architecture of the 6G-DALI project, which implements a federated dataspace and DataOps infrastructure to support secure, compliant, and scalable data sharing for AI-driven experimentation and service orchestration. Drawing from principles defined by GAIA-X and the International Data Spaces Association (IDSA), the architecture incorporates components such as federated identity management, policy-based data contracts, and automated data pipelines. We detail how the 6G-DALI architecture aligns with and extends GAIA-X and IDSA reference models to meet the unique demands of 6G networks, including low-latency edge processing, dynamic trust management, and cross-domain federation. A comparative analysis highlights both convergence points and necessary innovations.