An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-an...

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Main Authors: Yoo, Sunyong, Shin, Moonshik, Lee, Doheon
Format: Article
Language:English
Published: JMIR Publications 2012-11-01
Series:Interactive Journal of Medical Research
Online Access:http://www.i-jmr.org/2012/2/e14/
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spelling doaj-08252f830e5c41a99ec134124061d1092021-05-03T01:40:49ZengJMIR PublicationsInteractive Journal of Medical Research1929-073X2012-11-0112e1410.2196/ijmr.2140An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity MethodsYoo, SunyongShin, MoonshikLee, Doheon Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes.http://www.i-jmr.org/2012/2/e14/
collection DOAJ
language English
format Article
sources DOAJ
author Yoo, Sunyong
Shin, Moonshik
Lee, Doheon
spellingShingle Yoo, Sunyong
Shin, Moonshik
Lee, Doheon
An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
Interactive Journal of Medical Research
author_facet Yoo, Sunyong
Shin, Moonshik
Lee, Doheon
author_sort Yoo, Sunyong
title An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
title_short An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
title_full An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
title_fullStr An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
title_full_unstemmed An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods
title_sort approach to reducing information loss and achieving diversity of sensitive attributes in k-anonymity methods
publisher JMIR Publications
series Interactive Journal of Medical Research
issn 1929-073X
publishDate 2012-11-01
description Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes.
url http://www.i-jmr.org/2012/2/e14/
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