Growing synthetic data through differentially-private vine copulas
In this work, we propose a novel approach for the synthetization of data based on copulas, which are interpretable and robust models, extensively used in the actuarial domain. More precisely, our method COPULA-SHIRLEY is based on the differentially-private training of vine copulas, which are a famil...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2021-07-01
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| Series: | Proceedings on Privacy Enhancing Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/popets-2021-0040 |