zealscott/SynMeter
A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility. CCS 2025.
This tool helps researchers and data scientists systematically assess different methods for generating synthetic tabular datasets. You input your original, sensitive tabular data and it outputs multiple synthetic versions of that data, along with detailed evaluations of how well each synthetic version maintains the original data's characteristics, protects privacy, and is useful for analysis. This is for anyone who needs to create realistic, but anonymized, datasets for research, sharing, or development.
No commits in the last 6 months.
Use this if you need to compare and select the best method for creating synthetic tabular data while balancing data fidelity, privacy protection, and downstream utility.
Not ideal if you're looking for a simple, one-click solution to generate synthetic data without needing to understand or compare different synthesis algorithms and their detailed performance metrics.
Stars
26
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 17, 2025
Commits (30d)
0
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