Add dataset privacy risk assessment example notebook. (#73)

* Add dataset assessment notebook and reference to module from project README

Signed-off-by: Maya Anderson <mayaa@il.ibm.com>

README.md

@@ -16,6 +16,9 @@ minimization principle in GDPR for ML models. It enables to reduce the amount of
 personal data needed to perform predictions with a machine learning model, while still enabling the model
 to make accurate predictions. This is done by by removing or generalizing some of the input features.
 
+The [**dataset assessment**](apt/risk/data_assessment/README.md) module implements a tool for privacy assessment of
+synthetic datasets that are to be used in AI model training.
+
 Official ai-privacy-toolkit documentation: https://ai-privacy-toolkit.readthedocs.io/en/latest/
 
 Installation: pip install ai-privacy-toolkit

apt/risk/data_assessment/dataset_attack.py

@@ -87,7 +87,6 @@ class DatasetAttackMembership(DatasetAttack):
         labels = np.concatenate((np.zeros((len(non_member_probabilities),)), np.ones((len(member_probabilities),))))
         results = np.concatenate((non_member_probabilities, member_probabilities))
         svc_disp = RocCurveDisplay.from_predictions(labels, results)
-        svc_disp.plot()
         plt.plot([0, 1], [0, 1], color="navy", linewidth=2, linestyle="--", label='No skills')
         plt.title('ROC curve')
         plt.savefig(f'{filename_prefix}{dataset_name}_roc_curve.png')

requirements.txt

@@ -13,3 +13,8 @@ tensorflow==2.8.3
 xgboost==1.7.2
 Pillow==9.3.0
 sortedcontainers==2.4.0
+
+#notebooks
+notebook
+jupyter
+ipywidgets