Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective

Area of Science:

• Digital transformation and data economy
• Data privacy and security
• European data policy

Background:

• Societal digitization relies on data sharing, enabling services like online banking and healthcare access.
• Concerns over data control loss and reluctance to share sensitive personal and professional information hinder the data economy.
• The European Union's European Strategy for Data aims to create a single market for data, emphasizing trust and fairness.

Purpose of the Study:

• To review existing Privacy-Enhancing Technologies (PETs).
• To assess the relevance, technological maturity, and applicability of PETs within the context of common European data spaces.
• To illustrate the benefits of secure data sharing using Federated Learning in a privacy-critical healthcare use case.

Main Methods:

• Systematic review of current Privacy-Enhancing Technologies.
• Evaluation of PETs based on relevance, maturity, and applicability for European data spaces.
• Case study demonstrating Federated Learning for secure data sharing in healthcare.

Main Results:

• Identification and assessment of various PETs suitable for data spaces.
• Demonstration of PETs' capability to balance data sharing with privacy preservation.
• Successful application of Federated Learning in a healthcare scenario, ensuring privacy.

Conclusions:

• Privacy-Enhancing Technologies are crucial for the success of European data spaces.
• Federated Learning offers a viable solution for secure and private data sharing, particularly in sensitive sectors like healthcare.
• Continued development and adoption of PETs are essential for fostering a trustworthy data economy.