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update attack code

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This commit is contained in:
Liwei Song 2020-12-14 14:49:30 -05:00
parent b5b18de284
commit 3f40b8c465
1 changed files with 0 additions and 49 deletions
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tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py
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@ -30,7 +30,6 @@ from tensorflow_privacy.privacy.membership_inference_attack.data_structures impo
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import \
PrivacyReportMetadata
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import RocCurve
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import Seq2SeqAttackInputData
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleAttackResult
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleSliceSpec
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec
@ -175,54 +174,6 @@ def run_attacks(attack_input: AttackInputData,
privacy_report_metadata=privacy_report_metadata)
def run_seq2seq_attack(attack_input: Seq2SeqAttackInputData,
unused_report_metadata: PrivacyReportMetadata = None,
balance_attacker_training: bool = True) -> AttackResults:
"""Runs membership inference attacks on a seq2seq model.
Args:
attack_input: input data for running an attack
unused_report_metadata: the metadata of the model under attack.
balance_attacker_training: Whether the training and test sets for the
membership inference attacker should have a balanced (roughly equal)
number of samples from the training and test sets used to develop the
model under attack.
Returns:
the attack result.
"""
attack_input.validate()
# The attacker uses the average rank (a single number) of a seq2seq dataset
# record to determine membership. So only Logistic Regression is supported,
# as it makes the most sense for single-number features.
attacker = models.LogisticRegressionAttacker()
prepared_attacker_data = models.create_seq2seq_attacker_data(
attack_input, balance=balance_attacker_training)
attacker.train_model(prepared_attacker_data.features_train,
prepared_attacker_data.is_training_labels_train)
# Run the attacker on (permuted) test examples.
predictions_test = attacker.predict(prepared_attacker_data.features_test)
# Generate ROC curves with predictions.
fpr, tpr, thresholds = metrics.roc_curve(
prepared_attacker_data.is_training_labels_test, predictions_test)
roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds)
attack_results = [
SingleAttackResult(
slice_spec=SingleSliceSpec(),
attack_type=AttackType.LOGISTIC_REGRESSION,
roc_curve=roc_curve)
]
return AttackResults(single_attack_results=attack_results)
def _compute_privacy_risk_score(attack_input: AttackInputData,
num_bins: int = 15) -> SingleRiskScoreResult:
"""compute each individual point's likelihood of being a member (https://arxiv.org/abs/2003.10595)