This release is a pre-release and may not be stable for production use.
ML-PePR: Pentesting Privacy and Robustness
ML-PePR stands for Machine Learning Pentesting for Privacy and Robustness and is a Python library for evaluating machine learning models. PePR is easily extensible and hackable. PePR’s attack runner allows structured pentesting, and the report generator produces straightforward privacy and robustness reports (LaTeX/PDF) from the attack results.
Caution, we cannot guarantee the correctness of PePR. Always do check the plausibility of your results!
Installation
To install pepr use pip install mlpepr.
1 maintainer
Metadata
1 maintainer
Release files for mlpepr 0.1b6
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| mlpepr-0.1b6.tar.gz | 83.8 kB | Without provenance | Details for mlpepr-0.1b6.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mlpepr-0.1b6-py3-none-any.whl | Python 3 | none | any | Details for mlpepr-0.1b6-py3-none-any.whl |
Total release size: 174.7 kB
Release files / mlpepr-0.1b6.tar.gz
| Download URL | mlpepr-0.1b6.tar.gz |
|---|---|
| Size | 83.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / mlpepr-0.1b6-py3-none-any.whl
| Download URL | mlpepr-0.1b6-py3-none-any.whl |
|---|---|
| Size | 91.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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twine/3.4.1 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
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Security for mlpepr 0.1b6
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