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.
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