MP
privacytrustlab/ml_privacy_meter
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
706 149 +0/wk
GitHub
data-privacy data-protection data-protection-impact-assessment explainable-ai gdpr inference information-leakage machine-learning membership-inference-attack privacy privacy-audit
Trend
0
Star & Fork Trend (33 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
privacytrustlab/ml_privacy_meter has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | ml_privacy_meter | nimble | pymarl2 | DeepMesh |
|---|---|---|---|---|
| Stars | 706 | 706 | 706 | 706 |
| Forks | 149 | 71 | 135 | 33 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Jupyter Notebook | C++ | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | Apache-2.0 | Apache-2.0 | Apache-2.0 |
Capability Radar vs nimble
ml_privacy_meter
nimble
Maintenance Activity 0
Last code push 348 days ago.
Community Engagement 100
Fork-to-star ratio: 21.1%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.