gianlucatruda/TableDiffusion

The first differentially-private diffusion model for tabular data

40
/ 100
Emerging

This project helps data scientists and researchers create synthetic versions of sensitive tabular datasets, like customer records or medical histories. You input your original private data, and it outputs a new, artificially generated dataset that looks statistically similar but protects individual privacy. This is for anyone who needs to share or analyze data without revealing the original, private information.

No commits in the last 6 months.

Use this if you need to generate a privacy-preserving synthetic dataset from sensitive tabular data for research or analysis.

Not ideal if you require a production-ready solution for privacy guarantees, as this is currently for research purposes only.

data-privacy synthetic-data-generation data-sharing private-data-analysis differential-privacy
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 17 / 25

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Stars

33

Forks

9

Language

Python

License

GPL-3.0

Last pushed

Jun 05, 2024

Commits (30d)

0

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