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Add membership inference attack for seq2seq models

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This commit is contained in:
amad-person 2020-11-06 16:44:52 +08:00
parent cd57910e5c
commit d1c1746cdb
1 changed files with 45 additions and 2 deletions
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47
tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py
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@ -19,12 +19,13 @@ This file belongs to the new API for membership inference attacks. This file
will be renamed to membership_inference_attack.py after the old API is removed.
"""
from typing import Iterable
from typing import Iterable, Union
import numpy as np
from sklearn import metrics
from tensorflow_privacy.privacy.membership_inference_attack import models
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import Seq2SeqAttackInputData
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackResults
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackType
from tensorflow_privacy.privacy.membership_inference_attack.data_structures import \
@ -37,7 +38,7 @@ from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing impo
from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_slice
def _get_slice_spec(data: AttackInputData) -> SingleSliceSpec:
def _get_slice_spec(data: Union[AttackInputData, Seq2SeqAttackInputData]) -> SingleSliceSpec:
if hasattr(data, 'slice_spec'):
return data.slice_spec
return SingleSliceSpec()
@ -170,6 +171,48 @@ def run_attacks(attack_input: AttackInputData,
privacy_report_metadata=privacy_report_metadata)
def run_seq2seq_attack(attack_input: Seq2SeqAttackInputData,
privacy_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
privacy_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()
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=_get_slice_spec(attack_input),
attack_type=AttackType.LOGISTIC_REGRESSION,
roc_curve=roc_curve)]
return AttackResults(single_attack_results=attack_results)
def _compute_missing_privacy_report_metadata(
metadata: PrivacyReportMetadata,
attack_input: AttackInputData) -> PrivacyReportMetadata: