Knowledge Base

Applications & Related Techniques

This page gives an overview of techniques related to data spaces. So far, we have resources related to:

  • Artificial Intelligence (AI)
    Technology that enables computers and machines to simulate human intelligence and problem-solving capabilities [source].
  • Digital Product Passports (DPP)
    A tool to enable sharing of key product related information that are essential for products’ sustainability and circularity [source].
  • Privacy Enhancing Technologies (PET)
    Digital solutions that allow information to be collected, processed, analysed, and shared while protecting data confidentiality and privacy [source].
  • Self-Sovereign Identity (SSI)
    A technology that gives the user control over which personal and other data is shared [source].
Alex Preukschat and Drummond Reed
In Self-Sovereign Identity: Decentralized digital identity and verifiable credentials, you’ll learn how SSI empowers us to receive digitally-signed credentials, store them in private wallets, and securely prove our online identities. It combines a clear, jargon-free introduction to this blockchain-inspired paradigm shift with interesting essays written by its leading practitioners. Whether for property transfer, ebanking, frictionless travel, or personalized services, the SSI model for digital trust will reshape our collective future.

These slides explore the concept of Self-Sovereign Identity and its implications for individuals and organisations, along with insights into Europe’s plans regarding SSI.

K. Berger et al. (2023)

This document presents a concept for confidentiality-preserving data exchange to enhance sustainable product management using digital product passports (DPPs) in the context of electric vehicle batteries (EVBs). It explores data science and machine learning approaches to motivate stakeholders to share sensitive data, thereby supporting sustainable management decisions and facilitating a circular economy.

T. Adisorn, L. Tholen, T. Götz (2021)

This document discusses the concept of a Digital Product Passport (DPP) as a policy tool to enhance the circular economy by providing detailed product information throughout its lifecycle. The authors analyze current product information regimes, propose design options for the DPP, and emphasize the need for further research to address implementation challenges. The study highlights the potential benefits of the DPP for various stakeholders, including manufacturers, repair shops, and waste management companies, and calls for an integrated approach to leverage existing information systems and regulatory frameworks.

European Data Protection Supervisor

The purpose of this page is to provide an overview of Federated Learning as a method for developing machine-learning models.

S. Rossello, R. Díaz Morales, L. Muñoz-González (2021)

This article investigates some of the data protection implications of an emerging privacy preserving machine learning technique, i.e. federated machine learning. First, it shortly describes how this technique works and focuses on some of the main security threats it faces. Second, it presents some of the ways in which this technique can facilitate compliance with certain principles of the General Data Protection Regulation as well as some of the challenges it may pose under the latter.


The paper discusses the potential value of combining data from various sources for analysis in sectors like healthcare, public administration, and finance, despite challenges related to data sharing due to privacy legislation. It highlights Privacy-Enhancing Technologies (PETs) as a solution, enabling robust analysis of combined data while maintaining privacy. The report proposes integrating PETs into Data Spaces, leveraging research concepts to demonstrate how collaboration among parties can facilitate effective PET use beyond technological considerations, thus shaping future R&D agendas.


Data Spaces and Privacy Enhancing Technologies (PETs) have a common goal: making insights from data accessible in a confidential manner. But despite this overlap, the development of data spaces and PETs are driven by two different communities. According to Freek Bomhof and Harrie Bastiaansen, both consultants at TNO and affiliated with the CoE-DSC, this must change. Both Freek and Harrie were involved in the development of a joint Big Data Value Association (BDVA) and CoE-DSC whitepaper ‘Leveraging the benefits of combining data spaces and privacy enhancing technologies’. We spoke with them about why applying PETs within data spaces, confidentially exchanging insights from (privacy sensitive) data becomes more scalable.


Dit document left uit wat de implicaties en voordelen van Digitale Product Paspoorten (DPPs) zijn voor MKBs.


In dit whitepaper heeft het IP&T-team van Pels Rijcken – tezamen met technische experts van Linksight en TNO – deze juridische aspecten (in relatie tot de technische aspecten) van de inzet van MPC verkend. Gezien de grote hoeveelheid aan verschijningsvormen die MPC kent, is ervoor gekozen om twee specifieke decentrale MPC-toepassingen tot uitgangspunt te nemen in dit whitepaper, namelijk een specifieke vorm van ‘homomorfe encryptie’ (in dit whitepaper ook wel “decentrale homomorfe encryptie”) en een specifieke vorm van ‘secret sharing’, namelijk een die is gebaseerd op een ‘full threshold secret sharing scheme’ (in dit whitepaper ook wel aangeduid als “secret sharing”).

Netherlands AI Coalition (NL AIC)

This guide actively supports organisations with challenges regarding data sharing for AI applications. This guide focuses on the guidelines and building blocks to interconnect data spaces. 

Netherlands AI Coalition (NL AIC)

This guide actively supports organisations with challenges regarding data sharing for AI applications. In doing so, this guide focuses on the guidelines and building blocks for individual (sectoral, application-specific) AI data spaces.