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add comments to privacy risk scores

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This commit is contained in:
Liwei Song 2020-12-10 10:37:52 -05:00
parent bf65f55382
commit d1dcf56c44
1 changed files with 11 additions and 3 deletions
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14
tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
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@ -532,7 +532,7 @@ class SingleAttackResult:
@dataclass @dataclass
class SingleRiskScoreResult: class SingleRiskScoreResult:
"""Results from computing privacy risk scores. """Results from computing privacy risk scores.
this part is quite preliminary: it shows how to leverage privacy risk score to perform attacks with thresholding on risk score this part shows how to leverage privacy risk score to perform attacks with thresholding on risk score
""" """
# Data slice this result was calculated for. # Data slice this result was calculated for.
@ -543,6 +543,10 @@ class SingleRiskScoreResult:
test_risk_scores: np.ndarray test_risk_scores: np.ndarray
def attack_with_varied_thresholds(self, threshold_list): def attack_with_varied_thresholds(self, threshold_list):
""" For each threshold value, we count how many training and test samples with privacy risk scores larger than the threshold
and further compute precision and recall values.
We skip the threshold value if it is larger than every sample's privacy risk score.
"""
precision_list = [] precision_list = []
recall_list = [] recall_list = []
meaningful_threshold_list = [] meaningful_threshold_list = []
@ -553,9 +557,13 @@ class SingleRiskScoreResult:
meaningful_threshold_list.append(threshold) meaningful_threshold_list.append(threshold)
precision_list.append(true_positive_normalized/(true_positive_normalized+false_positive_normalized+0.0)) precision_list.append(true_positive_normalized/(true_positive_normalized+false_positive_normalized+0.0))
recall_list.append(true_positive_normalized) recall_list.append(true_positive_normalized)
return meaningful_threshold_list, precision_list, recall_list return np.array(meaningful_threshold_list), np.array(precision_list), np.array(recall_list)
def print_results(self, threshold_list=[1,0.9,0.8,0.7,0.6,0.5]): def print_results(self, threshold_list=np.array([1,0.9,0.8,0.7,0.6,0.5])):
""" The privacy risk score (from 0 to 1) represents each sample's probability of being in the training set.
Here, we choose a list of threshold values from 0.5 (uncertain of training or test) to 1 (100% certain of training)
to compute corresponding attack precision and recall.
"""
meaningful_threshold_list, precision_list, recall_list = self.attack_with_varied_thresholds(threshold_list) meaningful_threshold_list, precision_list, recall_list = self.attack_with_varied_thresholds(threshold_list)
for i in range(len(meaningful_threshold_list)): for i in range(len(meaningful_threshold_list)):
print(f"with {meaningful_threshold_list[i]} as the threshold on privacy risk score, the precision-recall pair is {(precision_list[i], recall_list[i])}") print(f"with {meaningful_threshold_list[i]} as the threshold on privacy risk score, the precision-recall pair is {(precision_list[i], recall_list[i])}")