ETSI GR PDL 034 V1.1.1 (2025-09)

Permissioned Distributed Ledger (PDL); Trustworthy Data Space Infrastructure with PDL

DGR/PDL-0034_Trust_Data_Space

General Information

• Status: Not Published
• Technical Committee: PDL - Permissioned Distributed Ledger
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 15-Oct-2025
• Completion Date: 18-Sep-2025

Standards Content (Sample)

GROUP REPORT
Permissioned Distributed Ledger (PDL);
Trustworthy Data Space Infrastructure with PDL
Disclaimer
The present document has been produced and approved by the Permissioned Distributed Ledger (PDL) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR PDL 034 V1.1.1 (2025-09)

Reference
DGR/PDL-0034_Trust_Data_Space
Keywords
data interoperability, data management,
data sharing, distributed ledger, PDL,
trustworthy AI
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3 ETSI GR PDL 034 V1.1.1 (2025-09)
Contents
Intellectual Property Rights . 7
Foreword . 7
Modal verbs terminology . 7
Executive summary . 7
Introduction . 8
1 Scope . 9
2 References . 9
2.1 Normative references . 9
2.2 Informative references . 9
3 Definition of terms, symbols and abbreviations . 10
3.1 Terms . 10
3.2 Symbols . 10
3.3 Abbreviations . 10
4 Trustworthy-Data-Space-Related Definitions . 11
4.1 Definition of Data Space . 11
4.1.1 Definition of Data . 11
4.1.2 Definition of Data Space . 11
4.2 Key Technology Concerning Data Space . 11
4.2.1 Distributed Architecture . 11
4.2.2 Blockchain . 11
4.2.3 Privacy Computing Technology . 11
4.2.4 Data Sovereignty Management . 12
4.2.5 Data Security Technology . 12
4.3 Definitions of Data-Related Processes . 12
4.3.1 Data Management . 12
4.3.2 Data Flow . 12
4.3.3 Data Supervision . 12
4.3.4 Data Storage. 12
4.3.5 Data Utilization . 12
4.3.6 Data Transaction . 12
4.3.7 Data Sharing . 12
4.3.8 Data Acquisition . 13
5 Introduction to Trustworthy Data Space Infrastructure . 13
5.1 Trustworthy Data Space . 13
5.1.1 Components of Trustworthy Data Space . 13
5.1.1.1 Data Security Infrastructure . 13
5.1.1.2 Trusted Data Flow Layer . 13
5.1.1.2.1 Trusted data flow platform . 13
5.1.1.2.2 Trusted data platform . 13
5.1.1.3 Trusted Data Application Layer . 13
5.2 The Functional Architecture of TDS . 14
5.2.1 Client . 14
5.2.1.1 Three Layers of Client . 14
5.2.1.2 Functionality . 14
5.2.2 Intermediate Service Platform . 15
5.2.2.1 Three Kinds of Services Available . 15
5.2.2.2 Functionality . 15
5.3 Emerging Technical Idea Supporting TDS . 16
5.3.1 Identity Management . 16
5.3.1.1 Types of Identity . 16
5.3.1.1.1 User public key identity . 16
5.3.1.1.2 Network identity . 16
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5.3.1.1.3 Application identity . 16
5.3.1.2 Potential Applications . 16
5.3.1.3 Existing Technology . 17
5.3.2 Data Storage and Validation . 17
5.3.2.1 Distributed Storage. 17
5.3.2.2 Consensus Mechanism . 17
5.3.3 Security and Privacy . 18
5.3.3.1 Cryptography Technology . 18
5.3.3.2 Privacy Protection . 18
5.3.4 Legal Compliance . 18
5.3.4.1 Anonymity and Regulatory Balance . 18
5.3.4.2 Data Self-Sovereignty . 18
5.3.5 Spatial and Time Reference Architecture for Data Space Network . 19
5.3.5.1 Hyperdimensional Simplex Fractal Network . 19
5.3.5.1.1 Introduction . 19
5.3.5.1.2 Architecture . 19
5.3.5.1.3 Consensus process . 19
5.3.5.1.4 Routing strategy. 19
5.3.5.2 Data Spaces Business Alliance Technical Convergence . 20
5.3.5.2.1 Introduction . 20
5.3.5.2.2 Architecture . 20
5.3.5.2.3 Consensus Process . 20
5.3.5.2.4 Routing Strategy . 21
5.3.5.3 International Data Space Reference Architecture Model . 21
5.3.5.3.1 Introduction . 21
5.3.5.3.2 Architecture . 22
5.3.5.3.3 Consensus Process . 22
5.3.5.3.4 Routing Strategy . 22
5.3.6 Web 3.0 Decentralized Controller for TDS . 23
5.3.6.1 Identity and Access . 23
5.3.6.2 Network and Application Integration . 23
5.3.6.3 Identity Association with Encrypted Address Translation . 24
5.3.6.4 Decentralized Services Sessions . 24
5.3.7 Trusted Timestamping Mechanism . 25
5.3.7.1 Principles . 25
5.3.7.2 Technical Implementation . 25
5.4 Core Features of TDS . 25
5.4.1 Decentralization . 25
5.4.2 Data Integrity . 26
5.4.3 Privacy Compliance . 26
5.4.4 Scalability . 26
6 Use Cases For Trustworthy Data Space Infrastructure . 26
6.1 Use Case 1 - Digital Product Passport . 26
6.2 Use Case 2 - Supply Chain Data Exchange . 27
6.3 Use Case 3 - Life-Cycle Management . 28
7 Development and Application of TDS . 28
7.1 Development Status . 28
7.1.1 Key Milestones in TDS Development . 28
7.1.2 Challenges and Limitations . 29
7.2 Future Trends . 29
7.2.1 Technological Innovation-Driven . 29
7.2.1.1 Efficient Consensus Mechanism . 29
7.2.1.2 Privacy Computing Fusion . 29
7.2.1.3 Collaboration Between Blockchain and AI . 29
7.2.2 Deep Integration of Law and Compliance . 30
7.2.2.1 Dynamic Compliance Framework . 30
7.2.2.2 Global Legal Alliance . 30
7.2.3 Multi-Field Application Expansion . 30
7.2.4 Infrastructure Optimization . 30
7.2.5 Standardization . 30
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7.2.5.1 Current Efforts . 30
7.2.5.2 Gaps and Roadmap . 30
7.3 Applications in Different Industries . 31
7.3.1 Communication Field . 31
7.3.2 Financial Field . 31
7.3.3 Industrial Field . 31
7.3.4 Energy Field. 31
7.3.5 Data Element Domain . 31
8 Conclusions and Recommendations . 32
8.1 Summary . 32
8.2 Recommendations . 32
Annex A: Bibliography . 34
History . 35

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List of figures
Figure 1: Components of Trustworthy Data Space .14
Figure 2: Client .15
Figure 3: Types of Identity .16
Figure 4: Data Spaces Business Alliance Technical Convergence .21
Figure 5: Architecture of IDS .22
Figure 6: Identity Hierarchy .23
Figure 7: Digital Product Passport .27

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Intellectual Property Rights
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Permissioned Distributed
Ledger (PDL).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Executive summary
The present document addresses the design, implementation, and governance of Trustworthy Data Spaces (TDS)
underpinned by Permissioned Distributed Ledger (PDL) technology. As data ecosystems grow increasingly complex,
the need for secure, interoperable, and regulatory-compliant frameworks becomes critical. TDS provides a decentralized
infrastructure that ensures data integrity, sovereignty, and privacy while enabling cross-organizational collaboration.
Key technologies explored include blockchain for immutable audit trails, privacy computing for confidential data
processing, and adaptive consensus mechanisms to balance scalability with security. The present document outlines
core components of TDS, such as layered architectures for trusted data flow, decentralized identity management, and
compliance with regulations like GDPR and MiCA.
Practical use cases, such as Digital Product Passports for circular economies, Supply Chain Data Exchange for real-time
transparency, and Life-Cycle Management for sustainability, demonstrate TDS's transformative potential across
industries. Challenges such as interoperability gaps, regulatory fragmentation, and legacy system integration are
discussed alongside future trends, including AI-blockchain convergence and global standardization efforts.
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The present document serves as a foundational guide for enterprises, policymakers, and technologists aiming to build
ethical, resilient, and scalable data ecosystems in alignment with emerging Web 3.0 paradigms.
Introduction
The present document focuses on the role of Permissioned Distributed Ledgers (PDL) in realizing TDS. Unlike public
blockchains, PDLs provide controlled access, enhanced scalability, and governance tailored to enterprise needs. By
integrating advanced cryptographic techniques, decentralized identity systems, and adaptive consensus models, TDS
enables secure data transactions, auditable workflows and privacy-preserving analytics across industries.

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1 Scope
The present document specifies the architectural framework, technical requirements, and implementation guidelines for
establishing Trustworthy Data Space (TDS) infrastructures utilizing Permissioned Distributed Ledger (PDL)
technology. It addresses the integration of decentralized systems, data sovereignty, security mechanisms, and
privacy-preserving technologies to enable secure, interoperable, and compliant data sharing across organizational and
jurisdictional boundaries.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document.
[i.1] 3GPP 23.501 (V19.3.0) (2025-03): "3rd Generation Partnership Project; Technical Specification
Group Services and System Aspects; System architecture for the 5G System (5GS); Stage 2
(Release 19)".
[i.2] 3GPP TS 33.501 (V19.2.0) (2025-03): "3rd Generation Partnership Project; Technical
Specification Group Services and System Aspects; Security architecture and procedures for 5G
System (Release 19)".
[i.3] 3GPP TR 33.794 (V19.1.0) (2025-03): "3rd Generation Partnership Project; Technical
Specification Group Services and System Aspects; Study on enablers for zero trust security
(Release 19)".
[i.4] Data Spaces Business Alliance: "Technical Convergence".
[i.5] European Data: "EU's Digital Product Passport: Advancing transparency and sustainability".
[i.6] Supplychainexchange.
[i.7] Life Cycle Initiative: "Life Cycle Management".
[i.8] Catena-X: "Sustainability with Catena-X".
[i.9] Digitaldefynd: "10 ways JP Morgan is using AI".
[i.10] Accenture: "Building a Responsible Metaverse".
[i.11] The data economy lab: "Cities & Data Sharing — Part 3: Barcelona".
[i.12] The HBS Digital Initiative: "Farm to Data Table: John Deere and Data in Precision Agriculture".
[i.13] Deterministic6G: "5G latency analysis and possible improvements".
[i.14] gridX: "Prosumer".
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[i.15] International Data Spaces.
[i.16] ISO/IEC DIS 27090: "Cybersecurity — Artificial Intelligence — Guidance for addressing security
threats to artificial intelligence systems".
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the following terms apply:
data: facts, observations, or values represented in any format
data security: protection of data from unauthorized access, corruption, theft, or misuse
distributed: system or architecture where components are spread across multiple nodes rather than centralized
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AI Artificial Intelligence
AML Anti-Money Laundering
API Application Programming Interface
APPID APPlication IDentity
BCADD BlockChain ADDress
BDVA Big Data Value Association
CA Certificate Authority
CIM cross cutting Context Information Management
DAGs Directed Acyclic Graphs
DGA Data Governance Act
DID Decentralized IDentifier
DLT Distributed Ledger Technology
DPP Digital Product Passport
DSBA Data Spaces Business Alliance
EBSI European Blockchain Services Infrastructure
ECC Elliptic Curve Cryptography
EHDS European Health Data Space
EPR Extended Producer Responsibility
ETSI European Telecommunications Standards Institute
GDPR General Data Protection Regulation
HE Homomorphic Encryption
IDSA International Data Spaces Association
IoT Internet of Things
IPFS InterPlanetary File System
JSON-LD JSON for Linked Data
KYC Know Your Customer
MAS Monetary Authority of Singapore
MFA Multi-Factor Authentication
MiCA Markets in Crypto-Assets Regulation
ML Machine Learning
MVF Minimum Viable Framework
NAT Network Address Translation
NFT Non-Fungible Token
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NGSI-LD Next Generation Service Interfaces - Linked Data
NIST National Institute of Standards and Technology
NIZK Non-Interactive Zero-Knowledge proof
ODRL Open Digital Rights Language
PBFT Practical Byzantine Fault Tolerance
PDP Policy Decision Points
PEP Policy Enforcement Points
PoA Proof of Authority
PoR Proof of Routing
PoS Proof of Stake
PoW Proof of Work
PQC Post-Quantum Cryptography
RDF Resource Description Framework
REST REpresentational State Transfer
SLA Service Level Agreement
SMPC Secure Multi-Party Computation
SSI Self-Sovereign Identity
TDS Trustworthy Data Space
VC Verifiable Credentials
ZKPs Zero-Knowledge Proofs
4 Trustworthy-Data-Space-Related Definitions
4.1 Definition of Data Space
4.1.1 Definition of Data
Data refers to structured or unstructured representations of facts, figures, or information in a form suitable for
processing, analysis, or communication. Data can be generated, stored, transmitted, and utilized through digital or
analog means, serving as the foundational element for decision-making, automation, and knowledge creation.
4.1.2 Definition of Data Space
A Data Space is a decentralized, interoperable ecosystem where data is shared, governed, and utilized under predefined
rules and technical frameworks. It ensures secure, sovereign, and privacy-preserving interactions among participants
while enabling seamless data exchange across organizational and technological boundaries.
4.2 Key Technology Concerning Data Space
4.2.1 Distributed Architecture
Distributed Architecture refers to a system design where data and computational resources are decentralized across
multiple nodes or entities. This architecture enhances scalability, fault tolerance, and resilience by eliminating single
points of failure and enabling peer-to-peer collaboration.
4.2.2 Blockchain
Blockchain is a decentralized database system based on Distributed Ledger Technology (DLT), constructed through a
distributed network of nodes, and consensus mechanisms.
4.2.3 Privacy Computing Technology
Privacy Computing Technology is the technology that enables data processing while preserving the privacy and
confidentiality of the data. Examples include homomorphic encryption, secure multi-party computation, and
zero-knowledge proofs.
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4.2.4 Data Sovereignty Management
Data sovereignty management refers to the fact that data collected or stored in a particular country or the European
Union is subject to the laws and governance of the country or the European Union of collection.
In Web 3.0, its main features are as follows:
1) Decentralization.
2) Users have their own autonomy and more control over their personal data and privacy.
4.2.5 Data Security Technology
Technologies and practices designed to protect data from unauthorized access, corruption, or theft. This includes
encryption, access control, and intrusion detection systems.
4.3 Definitions of Data-Related Processes
4.3.1 Data Management
Data Management involves the systematic organization, storage, and maintenance of data throughout its lifecycle. Key
activities include data cataloging, quality assurance, metadata management, and compliance auditing.
4.3.2 Data Flow
The movement of data between different systems or components within a data space. It includes data ingestion,
transformation and transmission.
4.3.3 Data Supervision
The monitoring and oversight of data usage to ensure compliance with policies and regulations. It includes
data auditing, data monitoring and data governance.
4.3.4 Data Storage
Data Storage encompasses technologies and methodologies for preserving data in physical or cloud-based repositories.
Considerations include redundancy, retrieval speed and energy efficiency.
4.3.5 Data Utilization
Data Utilization involves extracting value from data through analytics, machine learning, or visualization. It transforms
raw data into actionable insights for business, research or public services.
4.3.6 Data Transaction
Data Transaction is the exchange of data between parties under contractual or algorithmic agreements. Transactions
may involve monetary compensation, barter, or reciprocal data sharing, and its main function is reflected in:
1) Data transactions can ensure accurate transmission of information.
2) Data transactions enable real-time updates of data.
4.3.7 Data Sharing
Data Sharing enables collaborative use of data across organizations or systems while respecting ownership and privacy
constraints. It is facilitated by standardized APIs, licensing frameworks and trust mechanisms.
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4.3.8 Data Acquisition
Data Acquisition is the process of collecting data from sensors, databases, or external sources. It includes validation,
preprocessing, and integration steps to ensure data readiness for downstream applications.
5 Introduction to Trustworthy Data Space Infrastructure
5.1 Trustworthy Data Space
5.1.1 Components of Trustworthy Data Space
5.1.1.1 Data Security Infrastructure
Data security infrastructure is a framework of technologies, policies, and practices. As the foundational layer, data
security infrastructure ensures data confidentiality, integrity and availability through:
1) Encryption Mechanisms: End-to-end encryption for data at rest and in transit.
2) Access Control: Role-based and attribute-based policies to regulate data access.
3) Authentication & Authorization: Multi-Factor Authentication (MFA) and dynamic authorization aligned with
Zero Trust principles.
4) Audit Trails: Immutable logging of data interactions using PDL for traceability.
5.1.1.2 Trusted Data Flow Layer
5.1.1.2.1 Trusted data flow platform
The Trusted Data Flow Platform is a specialized system within the Trusted Data Flow Layer, designed to manage
high-volume, real-time data exchanges. Features include:
1) Interoperability Standards: Adherence to ETSI specifications.
2) Data Provenance: Metadata tagging to track data origin, ownership and lineage.
3) Smart Contracts: Automated enforcement of data-sharing agreements (e.g. compliance with predefined trust
thresholds).
5.1.1.2.2 Trusted data platform
Trusted data platform serves as a centralized repository for structured and unstructured data, equipped with governance
frameworks to ensure compliance and ethical use. It integrates tools for data cataloging, metadata management, and
role-based access control. Features include:
1) Distributed Ledger Integration: PDL-based storage for immutable records of data transactions.
2) Trust Index Management: Aggregation of trust indicators (e.g. security, reliability) to compute dynamic trust
scores.
3) Decentralized Governance: Consensus mechanisms for validating data transactions and policy updates.
5.1.1.3 Trusted Data Application Layer
This layer supports trust-aware applications and services through:
1) User-Centric Trust: Integration of Self-Sovereign Identity (SSI) for personalized trust evaluation.
2) Dynamic Trust Evaluation: Real-time assessment of entities using contextual data (location, behaviour).
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3) Trust-Enabled Use Cases:
- Collaborative AI/ML: Secure federated learning with verifiable contributor trust.
- Decentralized IoT: Device-to-device interactions governed by smart contracts.
- Regulatory Compliance: Automated adherence to eIDAS and GDPR via PDL-based audit trails.

Figure 1: Components of Trustworthy Data Space
5.2 The Functional Architecture of TDS
5.2.1 Client
5.2.1.1 Three Layers of Client
The client serves as the entry point for users to interact with the Trustworthy Data Space (TDS), with its architecture
being crucial for user experience and data security. The three-layer structure of the client consists of:
1) User Interface Layer: Responsible for interacting with users, providing a user-friendly interface for data access
and management.
2) Application Logic Layer: Manages business rules, workflows, and data processing tasks.
3) Data Access Layer: Communicates with the intermediate service platform to achieve data acquisition, storage,
and updates, ensuring data accuracy and timeliness.
5.2.1.2 Functionality
The main functions of the client include:
1) Data Access and Management: Users can query, download, and upload data within the TDS.
2) Data Security Protection: The client encrypts data to ensure its security during transmission and storage.
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3) Identity Authentication and Authorization: Manages user authentication and authorization to ensure only
legitimate users can access and manipulate data.
4) Data Interaction: Supports data sharing and collaborative work with other clients or servers.

Figure 2: Client
5.2.2 Intermediate Service Platform
5.2.2.1 Three Kinds of Services Available
The intermediate service platform is a core component of the TDS, offering various services to clients and data
platforms. The three primary services are:
1) Data Storage Service: Manages data storage and ensures data security and integrity.
2) Data Processing Service: Processes and analyses data, providing functions such as data mining and
transformation.
3) Data Transmission Service: Handles data transmission and distribution between components, ensuring
efficient and reliable data transfer.
5.2.2.2 Functionality
The main functions of the intermediate service platform include:
1) Service Management: Manages and schedules services on the platform to ensure smooth operation and
efficient resource use.
2) Data Routing and Forwarding: Routes and forwards data based on requests and targets for quick delivery.
3) Security Protection: Implements security measures like firewalls and intrusion detection to protect the platform
and data.
4) Monitoring and Maintenance: Monitors the platform's operating status in real-time, addressing potential issues
promptly to ensure stable operation.
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5.3 Emerging Technical Idea Supporting TDS
5.3.1 Identity Management
5.3.1.1 Types of Identity
5.3.1.1.1 User public key identity
In TDS, users use the public key as the identity, and there is no need for the Certificate Authority (CA) to recognize the
authenticity of the identity and the ownership of the public key, which has t
...