Estimating the success of re-identifications in incomplete datasets using generative models
Anonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices.
Main Authors: | , , |
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Format: | Article |
Language: | English |
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Nature Portfolio
2019-07-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-019-10933-3 |
_version_ | 1818992904192917504 |
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author | Luc Rocher Julien M. Hendrickx Yves-Alexandre de Montjoye |
author_facet | Luc Rocher Julien M. Hendrickx Yves-Alexandre de Montjoye |
author_sort | Luc Rocher |
collection | DOAJ |
description | Anonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices. |
first_indexed | 2024-12-20T20:33:34Z |
format | Article |
id | doaj.art-6c3c954c3a094ccd81b80c42685232b0 |
institution | Directory Open Access Journal |
issn | 2041-1723 |
language | English |
last_indexed | 2024-12-20T20:33:34Z |
publishDate | 2019-07-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Nature Communications |
spelling | doaj.art-6c3c954c3a094ccd81b80c42685232b02022-12-21T19:27:17ZengNature PortfolioNature Communications2041-17232019-07-011011910.1038/s41467-019-10933-3Estimating the success of re-identifications in incomplete datasets using generative modelsLuc Rocher0Julien M. Hendrickx1Yves-Alexandre de Montjoye2Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de LouvainInformation and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de LouvainDepartment of Computing, Imperial College LondonAnonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices.https://doi.org/10.1038/s41467-019-10933-3 |
spellingShingle | Luc Rocher Julien M. Hendrickx Yves-Alexandre de Montjoye Estimating the success of re-identifications in incomplete datasets using generative models Nature Communications |
title | Estimating the success of re-identifications in incomplete datasets using generative models |
title_full | Estimating the success of re-identifications in incomplete datasets using generative models |
title_fullStr | Estimating the success of re-identifications in incomplete datasets using generative models |
title_full_unstemmed | Estimating the success of re-identifications in incomplete datasets using generative models |
title_short | Estimating the success of re-identifications in incomplete datasets using generative models |
title_sort | estimating the success of re identifications in incomplete datasets using generative models |
url | https://doi.org/10.1038/s41467-019-10933-3 |
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