A scoping review of privacy and utility metrics in medical synthetic data
Abstract The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-related data beyond its initial collection while addressing privacy concerns. However, there is still no consensus on a standardized approach for systematically evaluating the privacy and utility...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01359-3 |
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| _version_ | 1832571325873389568 |
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| author | Bayrem Kaabachi Jérémie Despraz Thierry Meurers Karen Otte Mehmed Halilovic Bogdan Kulynych Fabian Prasser Jean Louis Raisaro |
| author_facet | Bayrem Kaabachi Jérémie Despraz Thierry Meurers Karen Otte Mehmed Halilovic Bogdan Kulynych Fabian Prasser Jean Louis Raisaro |
| author_sort | Bayrem Kaabachi |
| collection | DOAJ |
| description | Abstract The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-related data beyond its initial collection while addressing privacy concerns. However, there is still no consensus on a standardized approach for systematically evaluating the privacy and utility of synthetic data, impeding its broader adoption. In this work, we present a comprehensive review and systematization of current methods for evaluating synthetic health-related data, focusing on both privacy and utility aspects. Our findings suggest that there are a variety of methods for assessing the utility of synthetic data, but no consensus on which method is optimal in which scenario. Moreover, we found that most studies included in this review do not evaluate the privacy protection provided by synthetic data, and those that do often significantly underestimate the risks. |
| format | Article |
| id | doaj-art-b4a18c6e28ea4b6bab2a72a41756bc74 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b4a18c6e28ea4b6bab2a72a41756bc742025-02-02T12:43:36ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811910.1038/s41746-024-01359-3A scoping review of privacy and utility metrics in medical synthetic dataBayrem Kaabachi0Jérémie Despraz1Thierry Meurers2Karen Otte3Mehmed Halilovic4Bogdan Kulynych5Fabian Prasser6Jean Louis Raisaro7Biomedical Data Science Center, Centre Hospitalier Universitaire VaudoisBiomedical Data Science Center, Centre Hospitalier Universitaire VaudoisMedical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin BerlinMedical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin BerlinMedical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin BerlinBiomedical Data Science Center, Centre Hospitalier Universitaire VaudoisMedical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin BerlinBiomedical Data Science Center, Centre Hospitalier Universitaire VaudoisAbstract The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-related data beyond its initial collection while addressing privacy concerns. However, there is still no consensus on a standardized approach for systematically evaluating the privacy and utility of synthetic data, impeding its broader adoption. In this work, we present a comprehensive review and systematization of current methods for evaluating synthetic health-related data, focusing on both privacy and utility aspects. Our findings suggest that there are a variety of methods for assessing the utility of synthetic data, but no consensus on which method is optimal in which scenario. Moreover, we found that most studies included in this review do not evaluate the privacy protection provided by synthetic data, and those that do often significantly underestimate the risks.https://doi.org/10.1038/s41746-024-01359-3 |
| spellingShingle | Bayrem Kaabachi Jérémie Despraz Thierry Meurers Karen Otte Mehmed Halilovic Bogdan Kulynych Fabian Prasser Jean Louis Raisaro A scoping review of privacy and utility metrics in medical synthetic data npj Digital Medicine |
| title | A scoping review of privacy and utility metrics in medical synthetic data |
| title_full | A scoping review of privacy and utility metrics in medical synthetic data |
| title_fullStr | A scoping review of privacy and utility metrics in medical synthetic data |
| title_full_unstemmed | A scoping review of privacy and utility metrics in medical synthetic data |
| title_short | A scoping review of privacy and utility metrics in medical synthetic data |
| title_sort | scoping review of privacy and utility metrics in medical synthetic data |
| url | https://doi.org/10.1038/s41746-024-01359-3 |
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