gianlucatruda/TableDiffusion
The first differentially-private diffusion model for tabular data
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.
Stars
33
Forks
9
Language
Python
License
GPL-3.0
Category
Last pushed
Jun 05, 2024
Commits (30d)
0
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