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ScaDS Research on Scalable Privacy-preserving Record Linkage

  • Martin Franke
  • Marcel Gladbach
  • Ziad SehiliEmail author
  • Florens Rohde
  • Erhard Rahm
Schwerpunktbeitrag
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Abstract

Privacy-preserving record linkage (PPRL) supports the matching and integration of person-related data, e.g., on patients or customers without compromising privacy. It is based on the encoding of sensitive attribute values needed for matching and often involves trusted parties for linkage. We report on recent research results from the Big Data center ScaDS Dresden/Leipzig to improve the efficiency, scalability and quality of PPRL, and to apply PPRL in the medical domain. In particular, we present the use of pivot-based filtering techniques and LSH (locality-sensitive hashing)-based blocking to reduce the number of comparisons. Furthermore, we report on parallel linkage implementations based on Apache Flink supporting scalability to millions of records.

Keywords

Record Linkage Privacy Data Integration Blocking Metric Space LSH Apache Flink 

Notes

Acknowledgements

This work was partially funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions (ScaDS) Dresden/Leipzig (BMBF 01IS14014B).

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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Database Group, Institute of Computer ScienceUniversity of LeipzigLeipzigGermany

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