Multi-Party Privacy Preserving Record Linkage in Dynamic Metric Space
Ziad Sehili, Florens Rohde, Martin Michael Franke, Erhard Rahm · Gesellschaft für Informatik (GI) · 2021
We propose and evaluate several approaches for multi-party privacy-preserving record linkage (MP-PPRL) 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. 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.