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Value-specific Weighting for Record-level Encodings in Privacy-Preserving Record Linkage

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2023

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Gesellschaft für Informatik e.V.

Zusammenfassung

Privacy-preserving record linkage (PPRL) determines records representing the same entitywhile guaranteeing the privacy of individuals. A common approach is to encode plaintext data ofrecords into Bloom filters that enable efficient calculation of similarities. A crucial step of PPRL isthe classification of Bloom filter pairs as match or non-match based on computed similarities. In thecontext of record linkage, several weighting schemes and classification methods are available. Themajority of weighting methods determine and adapt weights by applying the Fellegi&Sunter modelfor each attribute. In the PPRL domain, the attributes of a record are encoded in a joint record-levelBloom filter to impede cryptanalysis attacks so that the application of existing attribute-wise weightingapproaches is not feasible. We study methods that use attribute-specific weights in record-levelencodings and integrate weight adaptation approaches based on individual value frequencies. Theexperiments on real-world datasets show that frequency-dependent weighting schemes improve thelinkage quality as well as the robustness with regard to the threshold selection.

Beschreibung

Rohde, Florens; Franke, Martin; Christen, Victor; Rahm, Erhard (2023): Value-specific Weighting for Record-level Encodings in Privacy-Preserving Record Linkage. BTW 2023. DOI: 10.18420/BTW2023-21. Bonn: Gesellschaft für Informatik e.V.. ISBN: 978-3-88579-725-8. pp. 439-460. Dresden, Germany. 06.-10. März 2023

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