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Auflistung nach Autor:in "Rohde, Florens"

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  • Textdokument
    Multi-Party Privacy Preserving Record Linkage in Dynamic Metric Space
    (BTW 2021, 2021) Sehili, Ziad; Rohde, Florens; Franke, Martin; Rahm, Erhard
    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.
  • Journal
    ScaDS Research on Scalable Privacy-preserving Record Linkage
    (Datenbank-Spektrum: Vol. 19, No. 1, 2019) Franke, Martin; Gladbach, Marcel; Sehili, Ziad; Rohde, Florens; Rahm, Erhard
  • Konferenzbeitrag
    Value-specific Weighting for Record-level Encodings in Privacy-Preserving Record Linkage
    (BTW 2023, 2023) Rohde, Florens; Franke, Martin; Christen, Victor; Rahm, Erhard
    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.