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I cant run the Notebook in the notebook folder.
With the help of @marisanest I installed a vitual environment based on python 3.9 and installed the Package with requirements.
But when I try to i…
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Parameters: model, training data and dict with keywords to perform attack
User membership inference attack from framework with defined (default)parameters
### Acceptance Criteria
Be able to perfo…
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**Describe the bug**
Incorrect import statements in _notebooks/attack_membership_inference.ipynb_
**To Reproduce**
Steps to reproduce the behavior:
Start the notebook _notebooks/attack_membershi…
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I have tried the default dataset of cifar100 and purchase100, for cifar100, it could reach a accuracy of 75%, while for purchase100, I could only get an accuracy of 52%, which is basiclly randomly gue…
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**Describe the bug**
Whenever you run _infer_ on _MembershipInferenceBlackBox_ you get a CUDA runtime error.
The _fit_ method seems to handle CUDA properly already. Noting the _to_cuda_ method.
…
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Hi, your research is really interesting! I am reproducing your experiments in CIFAR10. However, while the precision is able to achieve the performance in your paper, the recall rate is much lower than…
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Hallo,i have a question about generating shadow model training data.
you are using a query-based synthesis approach.However, the display result of the synthesis data is far from that of the cifar-10 …
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Hello Team Opacus , I would like to understand if there are any examples anywhere which demonstrates how training with DP could mitigate model Inversion attacks , membership inference and other privac…
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Hi, Dr. Song. I am now working on the privacy of federated learning, and I have read the article _Membership Inference Attacks Against Machine Learning Models_ . It is very kind of you for sharing the…
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Hello, there might be a trivial flaw in your code.
In line 39 of the file `ml_privacy_meter/tutorials/attack_alexnet.py`:
![image](https://user-images.githubusercontent.com/50874957/102001356-69e…