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Record linkage is the challenging task of deciding which records, coming from disparate data sources, refer to the same entity. Established back in 1946 by Halbert L. Dunn, the area of record linkage has received tremendous attention over the years due to its numerous real-world applications, and has led to a plethora of technologies, methods, metrics, and systems. A major direction in record linkage regards methods for linking records in a privacy-preserving manner, where sensitive and personally identifiable information in the records is not leaked as part of the linkage process. In this article, we provide an overview of the large body of research literature in privacy-preserving record linkage, discuss the different generations of techniques that have been proposed, their advantages and limitations, and present a taxonomy as well as an extensive survey on the latest generation of methods. We conclude this work with a roadmap to the new generation of analytics-driven techniques that aims to address some of the major challenges in the field.
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