Semi-local Time sensitive Anonymization of Clinical Data
Abstract A method for the anonymization of time-continuous data, which preserves the relation between the time- and value dimension is proposed in this work. The approach protects against linking- and distribution attacks by providing k-anonymity and t-closeness. Distributions can be generated from...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04192-1 |
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| _version_ | 1850102988930023424 |
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| author | Freimut Gebhard Herbert Hammer Mateusz Buglowski André Stollenwerk |
| author_facet | Freimut Gebhard Herbert Hammer Mateusz Buglowski André Stollenwerk |
| author_sort | Freimut Gebhard Herbert Hammer |
| collection | DOAJ |
| description | Abstract A method for the anonymization of time-continuous data, which preserves the relation between the time- and value dimension is proposed in this work. The approach protects against linking- and distribution attacks by providing k-anonymity and t-closeness. Distributions can be generated from given sets using Distribution Clustering, according to the similarity of the curves, which serve as a replacement for the population distribution. Before the data is anonymized, it is split along the time-axis using Windowed Fréchet Splitting, to reduce the duration and information loss. The proposed approach employs bucketization using the Fréchet distance with an implicit maximum cost and implied t for closeness and multiple redistribution phases. The information loss, median relative error and achieved t for the closeness is low, and the runtime was reduced with the introduction of semi-local decisions. |
| format | Article |
| id | doaj-art-217abecce01b47e2b99dd78c5b752a34 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-217abecce01b47e2b99dd78c5b752a342025-08-20T02:39:38ZengNature PortfolioScientific Data2052-44632024-12-0111112010.1038/s41597-024-04192-1Semi-local Time sensitive Anonymization of Clinical DataFreimut Gebhard Herbert Hammer0Mateusz Buglowski1André Stollenwerk2RWTH Aachen UniversityRWTH Aachen UniversityRWTH Aachen UniversityAbstract A method for the anonymization of time-continuous data, which preserves the relation between the time- and value dimension is proposed in this work. The approach protects against linking- and distribution attacks by providing k-anonymity and t-closeness. Distributions can be generated from given sets using Distribution Clustering, according to the similarity of the curves, which serve as a replacement for the population distribution. Before the data is anonymized, it is split along the time-axis using Windowed Fréchet Splitting, to reduce the duration and information loss. The proposed approach employs bucketization using the Fréchet distance with an implicit maximum cost and implied t for closeness and multiple redistribution phases. The information loss, median relative error and achieved t for the closeness is low, and the runtime was reduced with the introduction of semi-local decisions.https://doi.org/10.1038/s41597-024-04192-1 |
| spellingShingle | Freimut Gebhard Herbert Hammer Mateusz Buglowski André Stollenwerk Semi-local Time sensitive Anonymization of Clinical Data Scientific Data |
| title | Semi-local Time sensitive Anonymization of Clinical Data |
| title_full | Semi-local Time sensitive Anonymization of Clinical Data |
| title_fullStr | Semi-local Time sensitive Anonymization of Clinical Data |
| title_full_unstemmed | Semi-local Time sensitive Anonymization of Clinical Data |
| title_short | Semi-local Time sensitive Anonymization of Clinical Data |
| title_sort | semi local time sensitive anonymization of clinical data |
| url | https://doi.org/10.1038/s41597-024-04192-1 |
| work_keys_str_mv | AT freimutgebhardherberthammer semilocaltimesensitiveanonymizationofclinicaldata AT mateuszbuglowski semilocaltimesensitiveanonymizationofclinicaldata AT andrestollenwerk semilocaltimesensitiveanonymizationofclinicaldata |