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Multi-Party Privacy Preserving Record Linkage in Dynamic Metric Space

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Sehili, Z.; Rohde, F.; Franke, M.; Rahm, E.
Multi-Party Privacy Preserving Record Linkage in Dynamic Metric Space
Proc. 19. GI-Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW), 2021
2021-02

Description

We propose and evaluate several approaches for privacy-preserving record linkage for multiple data sources. To reduce the number of comparisons for scalability we propose a new pivot-based metric space approach that dynamically adapts the selection of pivots for additional sources and growing data volume. Furthermore, we investigate so-called early and late clustering schemes that either cluster matching records per additional source or holistically for all sources. A comprehensive evaluation for different datasets confirms the high effectiveness and efficiency of the proposed methods.