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Nov 27, 2018
Nov 21, 2018

Code style: black Binder

Membership Inference Attacks

Python package to create adversarial agents for membership inference attacks against machine learning models using Scikit-learn learners.

Implementation of the work done by Shokri et al (paper)

Examples

Find some examples in notebooks/

The main classes and functions are:

Data Synthetiser

To synthesize data only using a black-box like model target_model and predictions using the algorithm proposed by Shokri et al

from mblearn import synthetize

x = synthesize(target_model, fixed_class, k_max)

Shadow models

Train $n$ shadow models on synthetic data with a given learner. The learner must be a scikit-learn estimator with the predict_proba method.

from mblearn import ShadowModels

shadows = ShadowModels(n_models, data, target_classes, learner)

shadow_data = shadows.results

Attacker models

Using the data generated with the shadow models, trains a attack models on each label of the shadow dataset.

from mblearn import AttackModels

attacker = AttackModels(target_classes, attack_learner)

# train the attacker with the shadow data
attacker.fit(shadow_data)

# query the target model and get the predicted class prob vector
X = target_model.predict_proba(test_data)

# especulate about the class this test_data belongs to
y = 0

# get the prediction:
# True if `test_data` is classified as a member of
# the private model training set for the given class
# False otherwise
attacker.predict(X, y)

Bibliography

R. Shokri, M. Stronati, and V. Shmatikov. Membership inference attacks against machine learning models. Security and Privacy (SP), 2017 IEEE Symposium , IEEE, 2017.

Y. Long, V. Bindschaedler, L Wang, D. Bu, et al. Understanding Membership Inferences on Well-Generalized Learning Models. arXiv preprint arXiv:1802.04889, 2018.

S. Truex, L. Liu, M. E. Gursoy, L. Yu, W. Wei. Towards Demystifying Membership Inference Attacks. arXiv preprint arXiv:1807.09173, 2018.

Warning

The maturity of the package is far from alpha. This is just a proof of concept and all the interface and inner wheels will change in the next few months.

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Python package to create adversarial agents for membership inference attacks againts machine learning models

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