PePR is a library for pentesting the privacy risk and robustness of machine learning models.
Project description
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file mlpepr-0.1b6.tar.gz.
File metadata
- Download URL: mlpepr-0.1b6.tar.gz
- Upload date:
- Size: 83.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: 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
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 0f0892aeb26eab9ca0f2ee4ff09bd882cf87ac38563ee36c3961c88e75ca6a72 |
|
| MD5 | ff3b6b8ab9b5931bb0192ab8a5c0802c |
|
| BLAKE2b-256 | 91c7b1c0dcd1ba4d1fbce6dead12af891eff721ea53df3aed1d6890fae50d017 |
File details
Details for the file mlpepr-0.1b6-py3-none-any.whl.
File metadata
- Download URL: mlpepr-0.1b6-py3-none-any.whl
- Upload date:
- Size: 91.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: 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
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7c2b4d433d3ad77708564418012c4cb345b5655d57cf80b87b0f0e958d459378 |
|
| MD5 | 598449ed48d1f06bd22bd45f000e96bf |
|
| BLAKE2b-256 | b5636dbf2bcc944e5e8a46508ac548cdf02c4aa17c478ab4a6c370aed981d297 |
