Advanced record linkage methods and privacy aspects for population reconstruction-a survey and case studies

Peter Christen*, Dinusha Vatsalan, Zhichun Fu

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    8 Citations (Scopus)

    Abstract

    Recent times have seen an increased interest into techniques that allow the linking of records across databases. The main challenges of record linkage are (1) scalability to the increasingly large databases common today; (2) accurate and efficient classification of compared records into matches and non-matches in the presence of variations and errors in the data; and (3) privacy issues that occur when the linking of records is based on sensitive personal information about individuals. The first challenge has been addressed by the development of scalable indexing techniques, the second through advanced classification techniques that either employ machine learning- or graph-based methods, and the third challenge is investigated by research into privacy-preserving record linkage (PPRL). In this chapter, we describe these major challenges of record linkage in the context of population reconstruction. We survey recent developments of advanced record linkage methods, discuss two real-world case studies, and provide directions for future research.

    Original languageEnglish
    Title of host publicationPopulation Reconstruction
    PublisherSpringer International Publishing
    Pages87-110
    Number of pages24
    ISBN (Electronic)9783319198842
    ISBN (Print)9783319198835
    DOIs
    Publication statusPublished - 1 Jan 2015

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