Privacy-preserving record linkage on large real world datasets
Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to rel...
| Main Authors: | , , , , |
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| Format: | Journal Article |
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Academic Press
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/30680 |
| _version_ | 1848753157533859840 |
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| author | Randall, Sean Ferrante, Anna Boyd, James Bauer, J. Semmens, James |
| author_facet | Randall, Sean Ferrante, Anna Boyd, James Bauer, J. Semmens, James |
| author_sort | Randall, Sean |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised. |
| first_indexed | 2025-11-14T08:20:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-30680 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:03Z |
| publishDate | 2014 |
| publisher | Academic Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-306802017-09-13T15:06:34Z Privacy-preserving record linkage on large real world datasets Randall, Sean Ferrante, Anna Boyd, James Bauer, J. Semmens, James Record linkage Population based research Bloom filters Privacy preserving protocols Privacy preserving record linkage Data integration Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised. 2014 Journal Article http://hdl.handle.net/20.500.11937/30680 10.1016/j.jbi.2013.12.003 Academic Press unknown |
| spellingShingle | Record linkage Population based research Bloom filters Privacy preserving protocols Privacy preserving record linkage Data integration Randall, Sean Ferrante, Anna Boyd, James Bauer, J. Semmens, James Privacy-preserving record linkage on large real world datasets |
| title | Privacy-preserving record linkage on large real world datasets |
| title_full | Privacy-preserving record linkage on large real world datasets |
| title_fullStr | Privacy-preserving record linkage on large real world datasets |
| title_full_unstemmed | Privacy-preserving record linkage on large real world datasets |
| title_short | Privacy-preserving record linkage on large real world datasets |
| title_sort | privacy-preserving record linkage on large real world datasets |
| topic | Record linkage Population based research Bloom filters Privacy preserving protocols Privacy preserving record linkage Data integration |
| url | http://hdl.handle.net/20.500.11937/30680 |