SYNNER synthetic data generator framework
SYNNER provides a scalable and effective solution for generating synthetic data that maintains statistical fidelity. It overcomes the limitations of existing methods, providing a privacy-preserving solution for synthetic data generation and advancing research in sensitive domains such as healthcare.
Authors
- María Helena Franciscatto
- Justin M. Moore (ORCID: https://orcid.org/0000-0003-2385-6357)
- Hegler Tissot (ORCID: https://orcid.org/0000-0003-4635-451X)
- Marcos D Del Fabro
- Eric Benton
- Sarah Alshahrani
Institutions
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Laboratoire d'Intégration des Systèmes et des Technologies (FR)
- Universidade Federal do Paraná (BR)
- Drexel University (US)
Publication Details
- Journal
- Digital Health
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1177/20552076251411621
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 0.00