differential-privacy-prod
Pass
Audited by Gen Agent Trust Hub on Jun 16, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides standard mathematical implementations of differential privacy mechanisms such as Laplace and Gaussian noise. These are implemented locally using the numpy library with no external dependencies or network requirements.
- [SAFE]: External references target official documentation and repositories from trusted organizations including Google, Apple, and OpenDP.
- [SAFE]: Privacy budget management is handled through a local implementation that tracks epsilon and delta values without exposing sensitive data.
- [SAFE]: No suspicious command execution, persistence mechanisms, or obfuscation techniques were found in the scripts or documentation.
Audit Metadata