https://github.com/kruthikmanubolu/membership-inference-attack

https://github.com/kruthikmanubolu/membership-inference-attack

Last synced: 19 days ago
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• Host: GitHub
• URL: https://github.com/kruthikmanubolu/membership-inference-attack
• Owner: Kruthikmanubolu
• Created: 2024-03-20T00:25:43.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-20T02:06:33.000Z (10 months ago)
• Last Synced: 2024-11-28T21:11:45.282Z (about 1 month ago)
• Language: Python
• Size: 1.14 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### 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)`

This code is referenced from the paper : Song, Liwei and Prateek Mittal. “Systematic Evaluation of Privacy Risks of Machine Learning Models.” USENIX Security Symposium (2020).