- Home
- AI Synthetic Data Generators
- twinify
On the recordRead 5 Oct 202620 facts
twinify
On the record
- Data types
- DPVI can handle categorical, continuous, or mixed data; NAPSU-MQ is currently suitable only for fully categorical data.github.com · read 4 Oct 2026
- DPVI limitation
- DPVI supports categorical, continuous, and mixed data, but produces an approximate posterior and does not explicitly capture the additional uncertainty due to differential privacy.github.com · read 5 Oct 2026
- Inference methods
- It implements NAPSU-MQ and differentially private variational inference (DPVI).github.com · read 4 Oct 2026
- Installation
- The stable version is installed with pip from PyPI, and the development version can be installed from the cloned GitHub repository.github.com · read 5 Oct 2026
- Integrations
- The implementation relies on NumPyro for modeling and inference, JAX for CPU and GPU kernels, and d3p for differentially private training routines.github.com · read 5 Oct 2026
- Intended audience
- The package metadata classifies twinify for scientific research audiences.github.com · read 4 Oct 2026
- License
- The codebase is licensed under Apache License 2.0.github.com · read 5 Oct 2026
- Limitations
- NAPSU-MQ may run for a long time on datasets with many feature dimensions, while DPVI approximates the true posterior and does not explicitly capture additional uncertainty due to differential privacy.github.com · read 4 Oct 2026
- Maintainer organization
- The DPBayes GitHub organization describes itself as providing differential privacy software from the Finnish Center for Artificial Intelligence FCAI.github.com · read 4 Oct 2026
- Maker
- The DPBayes GitHub organization describes itself as differential privacy software from the Finnish Center for Artificial Intelligence (FCAI).github.com · read 5 Oct 2026
- Maker details
- The opened maker page does not state a headquarters or founding year.github.com · read 5 Oct 2026
- Methods
- It implements NAPSU-MQ and differentially private variational inference (DPVI).github.com · read 5 Oct 2026
- Missing values
- Automatic modeling handles missing values by assuming they are missing at random, independently of whether other feature values are missing.github.com · read 5 Oct 2026
- Modeling
- DPVI automatic modeling builds mixture models from user-specified feature distributions; users can also supply NumPyro models.github.com · read 5 Oct 2026
- NAPSU-MQ limitation
- NAPSU-MQ currently supports only fully categorical data and may run for a long time on datasets with many feature dimensions.github.com · read 5 Oct 2026
- Privacy controls
- Users can set ε and δ privacy parameters, with smaller values indicating stronger privacy.github.com · read 5 Oct 2026
- Privacy method
- It learns probabilistic models under differential privacy, with ε and δ parameters for setting the privacy level.github.com · read 4 Oct 2026
- Purpose
- twinify generates privacy-preserving synthetic twins of sensitive tabular datasets.github.com · read 5 Oct 2026
