Awesome

About

This code accompanies the paper "Systematic Evaluation of Privacy Risks of Machine Learning Models", accepted by USENIX Security 2021.

Usage

membership_inference_attacks.py contains the main membership inference attack code;
privacy_risk_score_utils.py contains the code to compute the privacy risk score for each individual sample.

In each folder, MIA_evaluate.py performs attacks against target machine learning classifiers.
If you want to further compute the privacy risk score, first import privacy_risk_score_utils.py; after initializing the attack class in MIA_evaluate.py, add risk_score = calculate_risk_score(MIA.s_tr_m_entr, MIA.s_te_m_entr, MIA.s_tr_labels, MIA.s_te_labels, MIA.t_tr_m_entr, MIA.t_tr_labels)

Impact

Our evaluation methods have been intergrated into Google's TensorFlow Privacy library, including both attack methods and the fine-grained individual privacy risk analysis.