Optimization of the Mainzelliste software for fast privacy-preserving record linkage
Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
15 January 2021
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| In: |
Journal of translational medicine
Year: 2021, Volume: 19, Pages: 1-12 |
| ISSN: | 1479-5876 |
| DOI: | 10.1186/s12967-020-02678-1 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s12967-020-02678-1
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| Author Notes: | Florens Rohde, Martin Franke, Ziad Sehili, Martin Lablans and Erhard Rahm |
| Summary: | Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. |
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| Item Description: | Gesehen am 03.05.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1479-5876 |
| DOI: | 10.1186/s12967-020-02678-1 |