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twinify

github.com

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Spec score 6.6 #23 of 25 in AI Synthetic Data Generators

Spec sheet

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

Where Specifiction places it

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