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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2052-4463 |