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.
%0 Conference Paper
%1 Sehili2021-me
%A Sehili, Ziad
%A Rohde, Florens
%A Franke, Martin
%A Rahm, Erhard
%D 2021
%I Gesellschaft für Informatik, Bonn
%K
%T Multi-party privacy preserving record linkage in dynamic metric space
%X 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.
@inproceedings{Sehili2021-me,
abstract = {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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Sehili, Ziad and Rohde, Florens and Franke, Martin and Rahm, Erhard},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2ef3733f06bed553c14e8ce3f3de30230/scadsfct},
interhash = {21d61c3c1fa4239653dfa3c5bc96eb5d},
intrahash = {ef3733f06bed553c14e8ce3f3de30230},
keywords = {},
publisher = {Gesellschaft f{\"u}r Informatik, Bonn},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {Multi-party privacy preserving record linkage in dynamic metric space},
year = 2021
}
