From 47bdc39d4bebe943eef578a36551c8fb00609ede Mon Sep 17 00:00:00 2001 From: Matteo Meucci Date: Sun, 23 Nov 2025 13:52:10 +0100 Subject: [PATCH] Update AITG-MOD-04_Testing_for_Membership_Inference.md --- ...-MOD-04_Testing_for_Membership_Inference.md | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/Document/content/tests/AITG-MOD-04_Testing_for_Membership_Inference.md b/Document/content/tests/AITG-MOD-04_Testing_for_Membership_Inference.md index f10405c..d7e3b46 100644 --- a/Document/content/tests/AITG-MOD-04_Testing_for_Membership_Inference.md +++ b/Document/content/tests/AITG-MOD-04_Testing_for_Membership_Inference.md @@ -28,19 +28,11 @@ This test identifies vulnerabilities to membership inference attacks, where adve - **Output Perturbation**: Add a small amount of noise to the model's output probabilities (confidence scores). This can help obscure the difference between member and non-member outputs, but it must be done carefully to avoid significantly impacting the model's utility. - **Knowledge Distillation**: Train a smaller "student" model to mimic a larger "teacher" model. The student model often does not have the same overfitting characteristics and can be more robust to these attacks. -### Suggested Tools for this Specific Test -- **Adversarial Robustness Toolbox (ART)** - - Provides explicit mechanisms for running membership inference attacks and evaluating model privacy. - - Tool Link: [ART on GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox) -- **ML Privacy Meter** - - A tool from Google specifically designed for evaluating privacy risks and membership inference vulnerabilities in machine learning models. - - Tool Link: [ML Privacy Meter on GitHub](https://github.com/privacytrustlab/ml_privacy_meter) -- **TensorFlow Privacy** - - A framework for training machine learning models with differential privacy guarantees, which is a primary defense against membership inference. - - Tool Link: [TensorFlow Privacy on GitHub](https://github.com/tensorflow/privacy) -- **Opacus** - - A library from Meta that enables training PyTorch models with differential privacy. - - Tool Link: [Opacus on GitHub](https://github.com/pytorch/opacus) +### Suggested Tools +- **Adversarial Robustness Toolbox (ART)**: Provides explicit mechanisms for running membership inference attacks and evaluating model privacy - [ART on GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox) +- **ML Privacy Meter**: A tool from Google specifically designed for evaluating privacy risks and membership inference vulnerabilities in machine learning models - [ML Privacy Meter on GitHub](https://github.com/privacytrustlab/ml_privacy_meter) +- **TensorFlow Privacy**: A framework for training machine learning models with differential privacy guarantees, which is a primary defense against membership inference - [TensorFlow Privacy on GitHub](https://github.com/tensorflow/privacy) +- **Opacus**: A library from Meta that enables training PyTorch models with differential privacy - [Opacus on GitHub](https://github.com/pytorch/opacus) ### References - Shokri, Reza, et al. "Membership Inference Attacks Against Machine Learning Models." IEEE Symposium on Security and Privacy (SP), 2017. [Link](https://www.cs.cornell.edu/~shmat/shmat_oak17.pdf)