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

Abstract

Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem like a minor cost for an individual user, but actually the data economy as the backbone of a digital transformation that is reshaping all aspects of human life. However, one of the major concerns arises regarding what happens to such individual data; once shared, control over it is often lost. For that reason, users and companies are reluctant to share their data. The European Union, through its European Strategy for Data, is establishing a policy and legal framework for establishing a single market for data in Europe by improving the trust and fairness of the data economy. Data spaces are a commitment to sharing data in a reliable and secure way, but this endeavour should, of course, not be at the expense of privacy rights. In recent years, Privacy-Enhancing Technologies (PETs) have emerged to achieve data sharing and privacy preservation that can address the requirements of data spaces around sensitive citizen and business data. In this work, we review existing PETs and assess their relevance, technological maturity, and applicability in the context of common European data spaces. Finally, we illustrate the benefits of secure data sharing via Federated Learning in a healthcare use case, where the preservation of privacy is a primer requirement and is therefore to be guaranteed.

Keywords

• Privacy preservation
• Federated learning
• Homomorphic encryption
• Differential privacy
• Data spaces