A systematic review of re-identification attacks on health data.

<h4>Background</h4>Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods...

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Autores principales:	Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, Bradley Malin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a97ec34e4155490d874171b9111d1f50
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spelling	oai:doaj.org-article:a97ec34e4155490d874171b9111d1f502021-11-18T07:33:12ZA systematic review of re-identification attacks on health data.1932-620310.1371/journal.pone.0028071https://doaj.org/article/a97ec34e4155490d874171b9111d1f502011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22164229/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods.<h4>Methods and findings</h4>Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046-0.478) and 0.34 for attacks on health data (95% CI 0-0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013.<h4>Conclusions</h4>The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.Khaled El EmamElizabeth JonkerLuk ArbuckleBradley MalinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e28071 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Khaled El Emam Elizabeth Jonker Luk Arbuckle Bradley Malin A systematic review of re-identification attacks on health data.
description	<h4>Background</h4>Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods.<h4>Methods and findings</h4>Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046-0.478) and 0.34 for attacks on health data (95% CI 0-0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013.<h4>Conclusions</h4>The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.
format	article
author	Khaled El Emam Elizabeth Jonker Luk Arbuckle Bradley Malin
author_facet	Khaled El Emam Elizabeth Jonker Luk Arbuckle Bradley Malin
author_sort	Khaled El Emam
title	A systematic review of re-identification attacks on health data.
title_short	A systematic review of re-identification attacks on health data.
title_full	A systematic review of re-identification attacks on health data.
title_fullStr	A systematic review of re-identification attacks on health data.
title_full_unstemmed	A systematic review of re-identification attacks on health data.
title_sort	systematic review of re-identification attacks on health data.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/a97ec34e4155490d874171b9111d1f50
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A systematic review of re-identification attacks on health data.

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