Privacy-preserving (health) data processing is possible!

The European Health Data Space is on the horizon, and it doesn't look like we can be satisfied with its implementation for now. Health data of all European insurance holders will be collected and retended not only for individual medical care, but also for scientific use. The so-called *secondary use* explicitly refers not only to academic research, but also to for-profit organizations. Not only universities will be able to access the data, but also, for example, the pharma industry and the big data companies such as Apple and Google. Claiming to improve the user experience of their proprietary health apps (anticipatory conjecture by the speakers), the most personal of all data will be placed in hands where it really does not belong to. So are we doomed? We say no! In this presentation, we will show how *probabilistic data structures* can be used to process personal data without compromising the privacy of individuals. We will show the results of a case study with exemplary health data. With this presentation, we want to point out that it is quite possible to give third parties certain access to health data, while preserving privacy for individuals.