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Update AITG-MOD-04_Testing_for_Membership_Inference.md

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Matteo Meucci
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@@ -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)