sassoftware/dpmm
dpmm: a library for synthetic tabular data generation with rich functionality and end-to-end Differential Privacy guarantees
This project helps data analysts and researchers create artificial datasets that look and behave like their original sensitive data, but without revealing any individual's private information. You feed it a tabular dataset, and it produces a new, synthetic tabular dataset that can be safely shared or analyzed without privacy risks. It's ideal for anyone who needs to work with sensitive data while adhering to strict privacy regulations or ethical guidelines.
Available on PyPI.
Use this if you need to generate high-quality synthetic tabular data that statistically mimics your real data, while rigorously protecting individual privacy through Differential Privacy.
Not ideal if you need a production-ready solution for complex, real-world applications beyond research or exploratory use, especially with datasets having more than 32 features.
Stars
13
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 26, 2026
Commits (30d)
0
Dependencies
8
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