Data Anonymization Tools

NFDI4Health develops comprehensive overview of data anonymization tools

To assist in the selection of appropriate solutions, TA6 has published an overview of tools for anonymizing tabular data in briefings in Bioinformatics in 2022.

Overview of the anonymization tools reviewed.

Task Area 6 "Privacy & Data Access in Concert" of NFDI4Health 2022 has compiled a comprehensive review of tools for anonymizing tabular data and published it in Briefings in Bioinformatics.

Anonymization can be an important component of data sharing processes and it helps protect the privacy of data subjects. This is a challenging task, where it is usually not sufficient to remove directly identifying features such as names. Instead, formal approaches that use mathematical or statistical models are needed to measure and reduce re-identification risks. However, such techniques are complex and should not usually be implemented from scratch. Instead, it is advisable to use existing and robust implementations. However, the range of available open-source tools is very heterogeneous, and it is not easy to get an overview of their strengths and weaknesses.

Based on a comparison of the anonymization techniques supported by the tools and other aspects, such as the maturity level, among others, recommendations for tools for the anonymization of medical datasets with different characteristics could be derived.

The results can assist NFDI4Health and other infrastructures in selecting appropriate anonymization tools.

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Health Study Hub

The German Central Health Study Hub allows researchers to publish their project characteristics, documents and data related to their research project in a FAIR manner or to find information about past and ongoing studies.

Data Train

The Data Train cross-disciplinary graduate training programme, a core element of the NFDI4Health training approach, aims at building the next generation of data-savvy researchers in the biomedical sciences.

Personal Health Train

To foster data-driven innovation in medicine, we developed a distributed analysis infrastructure that enables research on sensitive data without prior data sharing while supporting diverse data formats.

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Health data, as collected in clinical trials and epidemiological, as well as public health studies, cannot be freely published, but are valuable datasets whose reuse is of high importance for health research. NFDI4Health has established a metadata standard and process for the publication of health studies to make health data FAIR.

Data harmonisation

To make health studies and their data FAIR we have developed guidelines and standards for metadata description and data sharing. We have developed data publication guidelines, common metadata description standards and adaptations of health data interoperability standards to harmonize the description of studies and their corresponding metadata.

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It is a paradox: on the one hand, good scientific work depends on high data quality. On the other hand, a lot of effort is put into the design and conduct of studies, but not into data quality assessments. We help to facilitate the efficient performance of such assessments with versatile concepts and tools

DataSHIELD

Expansion of decentralised research projects with DataSHIELD: Until now, data protection concerns and the lack of special IT infrastructure have prevented the expansion of cross-institutional research projects. DataSHIELD is intended to solve that problem.