diff --git a/apt/anonymization/anonymizer.py b/apt/anonymization/anonymizer.py
index 8a4f95d..94eb403 100644
--- a/apt/anonymization/anonymizer.py
+++ b/apt/anonymization/anonymizer.py
@@ -45,10 +45,10 @@ class Anonymize:
         :return: An array containing the anonymized training dataset.
         """
         if dataset.features_names is not None:
-            self._features = dataset.features_names
+            self.features_names = dataset.features_names
             # if features is None, use numbers instead of names
         elif dataset.get_samples().shape[0] != 0:
-            self._features = [i for i in range(dataset.get_samples().shape[0])]
+            self.features_names = [i for i in range(dataset.get_samples().shape[0])]
         else:
             raise ValueError('No data provided')
         if not set(self.quasi_identifiers).issubset(set(self.features_names)):
@@ -63,7 +63,7 @@ class Anonymize:
 
         transformed = self._anonymize(dataset.get_samples().copy(), dataset.get_labels())
         if dataset.is_pandas:
-            return pd.DataFrame(transformed, columns=self._features)
+            return pd.DataFrame(transformed, columns=self.features_names)
         else:
             return transformed
 
diff --git a/tests/test_anonymizer.py b/tests/test_anonymizer.py
index 83710cd..464bd20 100644
--- a/tests/test_anonymizer.py
+++ b/tests/test_anonymizer.py
@@ -41,9 +41,7 @@ def test_anonymize_pandas_adult():
           'native-country']
     categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
                             'native-country']
-    QI_indexes = [i for i, v in enumerate(features) if v in QI]
-    categorical_features_indexes = [i for i, v in enumerate(features) if v in categorical_features]
-    anonymizer = Anonymize(k, QI_indexes, categorical_features=categorical_features_indexes)
+    anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
     anon = anonymizer.anonymize(ArrayDataset(x_train, pred, features))
 
     assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])
@@ -62,9 +60,7 @@ def test_anonymize_pandas_nursery():
     k = 100
     QI = ["finance", "social", "health"]
     categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
-    QI_indexes = [i for i, v in enumerate(features) if v in QI]
-    categorical_features_indexes = [i for i, v in enumerate(features) if v in categorical_features]
-    anonymizer = Anonymize(k, QI_indexes, categorical_features=categorical_features_indexes)
+    anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
     anon = anonymizer.anonymize(ArrayDataset(x_train, pred))
 
     assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])