Technological Enablers for Privacy Preserving Data Sharing and Analysis

As data becomes more important, so does the need to protect privacy, in the sense of protecting the personal, confidential and/or private information that it contains. Privacy Enhancing Techniques (PETs) are a key enabler technology for ensuring that privacy is maintained while extracting value from the data. The first objective of this report is to analyse various PETs with an assessment of their usability and maturity in the context of data sharing scenarios, and in particular within common European data spaces. The second objective is to demonstrate the application of one PET in a collaborative scenario between different entities. Over the course of this analysis, these objectives were accomplished in two phases: first, a detailed state-of-the-art analysis and evaluation of the pros, cons, and maturity (i.e., their TRL level) of the different PETs was carried out, in order to select the one of greatest interest to propose a collaborative scenario between different entities. Secondly, a realistic use case from the healthcare domain, therefore relevant to the European Health Data Space was implemented with the selected PET (Federated Learning) and the results were evaluated