Künstliche Intelligenz und sichere Gesundheitsdatennutzung im Projekt KI-FDZ: Anonymisierung, Synthetisierung und sichere Verarbeitung für Real-World-Daten

The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic – that is, artificial – data generated by AI can protect privacy.

This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data.