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Add an example for the group-wise attacks; Update README.md.
Use pd.testing.assert_index_equal instead of list comparison.
MatteoGiomi
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Mar 27, 2026
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Thanks @itrajanovska, looks very good! I have just a few comments.
| "\n", | ||
| "The synthetic version has been generated by `CTGAN` from [SDV](https://sdv.dev/SDV/user_guides/single_table/ctgan.html), as explained in the paper accompanying this code release. For details on the generation process, e.g. regarding hyperparameters, see Section 6.2.1 of [the accompanying paper](https://petsymposium.org/popets/2023/popets-2023-0055.php)).\n", | ||
| "\n", | ||
| "We pull these datasets from the [Statice](https://www.statice.ai/) public GC bucket:" |
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this can be removed, the data is distribted with the code now.
| "The requirements for the model are:\n", | ||
| "\n", | ||
| "1. It is trained on the synthetic data with the `secret` as a target. (It can be a classification or regression model)\n", | ||
| "2. The inference model must implement our protocol that defines a predict(x) method returning predictions whose index matches that of x (anonymeter.evaluators.inference_predictor::InferencePredictor)\n", |
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the reference to the InferencePredictor does not render well.
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Wrapped it in `` now
| " inference_model = SklearnInferencePredictor(model)\n", | ||
| " \n", | ||
| " # Needed for anonymeter (is the target categorical or numerical variable?)\n", | ||
| " regression = True if syn[secret].dtype.kind in 'iuf' else False\n", |
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if you leave it to the default (None) the code will decide depending or not the secret is numerical (see here) using a similar logic.
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Thanks, I oversaw this. The parameter is removed now
Remove explicit regression parameter in InferenceEvaluator.
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Add an example with the custom model feature for the inference attacks;
Add an example for the group-wise attacks;
Update README.md.