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 |
| Published: |
Nature Portfolio
2019-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-10933-3 |
| Summary: | 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. |
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| ISSN: | 2041-1723 |