Encoding of numerical data for privacy-preserving record linkage

Lea Demelius*, Karl Kreiner, Dieter Hayn, Michael Nitzlnader, Günter Schreier

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Background: Privacy-preserving record linkage (PPRL) is the process of detecting dataset entries that refer to the same individual within two independent datasets, without disclosing any personal information. While applied in different fields, it particularly attained importance in the medical sector. One popular PPRL method are Bloom filters. However, Bloom filters were originally used for encoding strings only. Objectives: This paper evaluates an encoding method specifically designed for numerical data and adjusts it for encoding geocoordinates in Bloom filters. Methods: The proposed numerical encoding of geocoordinates is compared to the string-based method by using synthetic data. Results: The proposed method for encoding geocoordinates in Bloom filters attains a higher recall and precision than the conventional string encoding. Conclusion: Numerical encoding has the potential of increasing the record linkage quality of Bloom filters, as well as their privacy level.

Originalspracheenglisch
TiteldHealth 2020 - Biomedical Informatics for Health and Care
UntertitelProceedings of the 14th Health Informatics Meets Digital Health Conference
Redakteure/-innenGunter Schreier, Dieter Hayn, Alphons Eggerth
Herausgeber (Verlag)IOS Press BV
Seiten23-30
Seitenumfang8
ISBN (elektronisch)9781643680842
DOIs
PublikationsstatusVeröffentlicht - 23 Jun 2020

Publikationsreihe

NameStudies in Health Technology and Informatics
Band271
ISSN (Print)0926-9630
ISSN (elektronisch)1879-8365

ASJC Scopus subject areas

  • !!Biomedical Engineering
  • !!Health Informatics
  • !!Health Information Management

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