LogoLogo
How to Build Dataspaces?Main IDSA AssetsOther ResourcesManifesto for International Data Spaces
IDS-RAM 4
IDS-RAM 4
  • README
  • Front Matter
    • Front Matter
    • Contributing Projects
  • Introduction
    • 1. Introduction
      • 1.1 Goals of the International Data Spaces
      • 1.2 Purpose and Structure of the Reference Architecture
      • 1.3 Relation to other IDSA assets
  • Context of the International Data Spaces
    • 2. Context of the International Data Spaces
      • 2.1 Data-Driven Business Ecosystems
      • 2.2 Data Sovereignty as a Key Capability
      • 2.3 Data as an Economic Good
      • 2.4 Data Exchange and Data Sharing
      • 2.5 Meaningful data
      • 2.6 Industrial Cloud Platforms
      • 2.7 Big Data and Artificial Intelligence
      • 2.8 The Internet of Things and the Industrial Internet of Things
      • 2.9 Blockchain
      • 2.10 Federated frameworks for data sharing agreements and terms of use
      • 2.11 General Data Protection Regulation
      • 2.12 Contribution of the International Data Spaces to Industry 4.0 and the Data Economy
      • 2.13 Privacy in the connected world
  • Layers of the Reference Architecture Model
    • 3 Layers of the Reference Architecture Model
      • 3.1 Business Layer
        • 3.1.1 Roles in the International Data Spaces
        • 3.1.2 Interaction of Roles
        • 3.1.3 Digital Identities
        • 3.1.4 Usage Contracts
      • 3.2 Functional Layer
      • 3.3 Information Layer
      • 3.4 Process Layer
        • 3.4.1 Onboarding
        • 3.4.2 Data Offering
        • 3.4.3 Contract Negotiation
        • 3.4.4 Exchanging Data
        • 3.4.5 Publishing and using Data Apps
        • 3.4.6 Policy Enforcement
      • 3.5 System Layer
        • 3.5.1 Identity Provider
        • 3.5.2 IDS Connector
        • 3.5.3 App Store and App Ecosystem
        • 3.5.4 Metadata Broker
        • 3.5.5 Clearing House
        • 3.5.6 Vocabulary Hub
  • Perspectives of the Reference Architecture Model
    • 4 Perspectives of the Reference Architecture Model
      • 4.1 Security Perspective
        • 4.1.1 Security Aspects addressed by the different Layers
        • 4.1.2 Identity and Trust Management
        • 4.1.3 Securing the Platform
        • 4.1.4 Securing Applications
        • 4.1.5 Securing Interactions between IDS components
        • 4.1.6 Usage Control
      • 4.2 Certification Perspective
        • 4.2.1 Certification Aspects Addressed by the Different Layers of the IDS-RAM
        • 4.2.2 Roles
        • 4.2.3 Operational Environment Certification
        • 4.2.4 Component Certification
        • 4.2.5 Processes
      • 4.3 Data Governance Perspective
        • 4.3.1 Governance Aspects Addressed by the Different Layers of the IDS-RAM
        • 4.3.2 Data Governance Model
        • 4.3.3 Data as an Economic Good
        • 4.3.4 Data Ownership
        • 4.3.5 Data Sovereignty
        • 4.3.6 Data Quality
        • 4.3.7 Data Provenance
        • 4.3.8 Data Space Instances
        • 4.3.9 IDS Rulebook
        • 4.3.10 Privacy Perspective
        • 4.3.11 Governance for Vocabularies
Powered by GitBook
On this page
Edit on GitHub
  1. Context of the International Data Spaces
  2. 2. Context of the International Data Spaces

2.9 Blockchain

Last updated 2 years ago

Links:

  • IDSA Website
  • IDSA Github
  • Legal Notice
  • Privacy Policy

© 2016 – 2025 | All Rights Reserved | International Data Spaces Association

The core purpose of the International Data Spaces is to enable controlled exchange and sharing of data between organizations -- regardless of the type of data. In many use cases of the International Data Spaces, this is some form of structured data (e.g. measurement data, product data, or logistics data). But also other types of (streaming) data are supported. The IDS Connector allows data owners and data providers to exchange and share their data with other participants in the IDS ecosystem, while data sovereignty is ensured at any time.

In the use cases of the International Data Spaces, two basic patterns of data sharing can be found:

  • Data is shared to feed new, data-driven services, such as using the data in a new app, smart algorithm, or other digital service in which data of different sources/providers is combined.

  • Data is shared for some form of business process synchronization, such as using the data to execute transactions (e.g. exchange orders), enable production (e.g. exchange product data), check quality (e.g. monitor the temperature of perishable goods), or synchronize processes (e.g. exchange status data).

In many of these cases, this sharing of data enables transactions with the data itself becoming what one could call a 'shared data asset', resulting in liability/responsibility for the participating organizations.

Two examples:

  • As perishable goods were exposed to improper ambient temperatures, the company ordering the goods refuses acceptance. The temperature data thereby becomes a shared data asset that can be stored in a shared environment which acts as a trusted record keeper of such quality data.

  • Several companies want to share their capabilities in order to produce a certain type of good. In this case, the capability of each company becomes a shared data asset to be stored in shared 'yellow pages' accessible for all participants in the ecosystem.

From a functional perspective, it is expected that blockchain technology will play an important role in maintaining these 'shared data assets' in an IDS environment. This would complement the existing capabilities of the IDS architecture to share (potentially large) datasets with the help of IDS Connectors. For instance, a shared data asset might encompass a hash code ('fingerprint' of a piece of data) which can be used to verify a larger file (e.g. a complex product design for which an order was sent) being shared with the help of an IDS Connector. In terms of the IDS-RAM, blockchain technology could be used for the Clearing House or the Metadata Broker, for example (see Business Layer).

In general, the use of Blockchain technology can ensure data consistency and transparency in combination with the general IDS approach for data sovereignty and secure data exchange and sharing. In contrast, typical Data Lakes focus on the integration of data for the purpose of knowledge extraction (see Figure below).

Figure 2.9 : General architectural patterns for data exchange and data sharing

General architectural patterns for data exchange and data sharing