Privacy-preserving record linkage

Dinusha Vatsalan; Dimitrios Karapiperis; Vassilios Verykios; Sherif Sakr and Albert Zomaya

doi:10.1007/978-3-319-63962-8_17-1

Privacy-preserving record linkage

Dinusha Vatsalan^*, Dimitrios Karapiperis, Vassilios Verykios, Sherif Sakr and Albert Zomaya

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Given several databases containing person-specific data held by different organizations, privacy-preserving record linkage (PPRL) aims to identify and link records that correspond to the same entity/individual across different databases based on the matching of personal identifying attributes, such as name and address, without revealing the actual values in these attributes due to privacy concerns. The output is a set of matching clusters containing records of the same entity.

Original language	English
Title of host publication	Encyclopedia of Big Data Technologies
Editors	Sherif Sakr, Albert Zomaya
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	1-8
Number of pages	8
Edition	Living Edition
ISBN (Electronic)	9783319639628
DOIs	https://doi.org/10.1007/978-3-319-63962-8_17-1
Publication status	Published - 7 Feb 2018
Externally published	Yes

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10.1007/978-3-319-63962-8_17-1

Cite this

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abstract = "Given several databases containing person-specific data held by different organizations, privacy-preserving record linkage (PPRL) aims to identify and link records that correspond to the same entity/individual across different databases based on the matching of personal identifying attributes, such as name and address, without revealing the actual values in these attributes due to privacy concerns. The output is a set of matching clusters containing records of the same entity.",

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AB - Given several databases containing person-specific data held by different organizations, privacy-preserving record linkage (PPRL) aims to identify and link records that correspond to the same entity/individual across different databases based on the matching of personal identifying attributes, such as name and address, without revealing the actual values in these attributes due to privacy concerns. The output is a set of matching clusters containing records of the same entity.

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Privacy-preserving record linkage

Abstract

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