Growing synthetic data through differentially-private vine copulas
| Autor: | Antoine Laurent, Frédéric Ladouceur, Sébastien Gambs, Alexandre Roy-Gaumond |
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| Rok vydání: | 2021 |
| Předmět: |
Ethics
copulas Computer science privacy evaluation QA75.5-76.95 02 engineering and technology synthetic data BJ1-1725 01 natural sciences Synthetic data Vine copula 010104 statistics & probability differential privacy Electronic computers. Computer science 020204 information systems 0202 electrical engineering electronic engineering information engineering Econometrics General Earth and Planetary Sciences 0101 mathematics General Environmental Science |
| Zdroj: | Proceedings on Privacy Enhancing Technologies, Vol 2021, Iss 3, Pp 122-141 (2021) |
| ISSN: | 2299-0984 |
| Popis: | 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 family of copulas allowing to model and generate data of arbitrary dimensions. The framework of COPULA-SHIRLEY is simple yet flexible, as it can be applied to many types of data while preserving the utility as demonstrated by experiments conducted on real datasets. We also evaluate the protection level of our data synthesis method through a membership inference attack recently proposed in the literature. |
| Databáze: | OpenAIRE |
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