-
I'm using fbleau on log-losses features for membership inference attacks (mia), and I noticed that fbleau consistently beats random guessing on control models: constant (losses = np.ones) and random. …
-
**Is your feature request related to a problem? Please describe.**
Would be interesting to compare to Choquette-Choo et al.'s work which is already implemented (`LabelOnlyDecisionBoundary`).
**Des…
-
Hey,
I am trying to reproduce the Figure-15. Is it possible to share the source code for membership inference attacks from your paper.
Thanks in advance for your help.
-
It seems like the current config file does not contain "n_attacker_train", "n_attacker_test" that are required in mia_attacks.py. Are these two parameters representing the number of samples used to es…
-
Work on getting membership inference added to the framework so that it can be used to test joe's differential privacy when that is ready.
-
Currently the privacy risk score calculation is done by running an implicit MIA when calling the `compute_privacy_risk_score` function. It should be clear that this actually is an actual MIA and not j…
-
It conducts a membership inference attack on the points that calculates the privacy risk score per data point.
*Write a notebook as an example implementation.
-
I've looked at some other python repos and people seem to never do `import full_package.module` but instead `import .module`. And @adrinjalali also recommended to use local imports.
So maybe that's j…
-
* Slicing correctly classified vs wrongly classified
* Slicing per class
* Output: should give attack advantage score for the whole dataset, and for each slice individually
* Have slices as optiona…
-
Is it possible to just test through the test data? Also Is there anyway to do this in batches?