domias 0.0.5

DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.

DOMIAS: Membership Inference Attacks against Synthetic Data through Overfitting Detection

Installation

The library can be installed from PyPI using

$ pip install domias

or from source, using

$ pip install .

API

The main API call is

from domias.evaluator import evaluate_performance

evaluate_performance expects as input a generator which implements the domias.models.generator.GeneratorInterface interface, and an evaluation dataset.

The supported arguments for evaluate_performance are:

  generator: GeneratorInterface
      Generator with the `fit` and `generate` methods. The generator MUST not be fitted.
  dataset: int
      The evaluation dataset, used to derive the training and test datasets.
  mem_set_size: int
      The split for the training dataset out of `dataset`
  reference_set_size: int
      The split for the reference dataset out of `dataset`.
  training_epochs: int
      Training epochs
  synthetic_sizes: List[int]
      For how many synthetic samples to test the attacks.
  density_estimator: str, default = "prior"
      Which density to use. Available options:
          * prior
          * bnaf
          * kde
  seed: int
      Random seed
  device: PyTorch device
      CPU or CUDA
  shifted_column: Optional[int]
      Shift a column
  zero_quantile: float
      Threshold for shifting the column.
  reference_kept_p: float
      Held-out dataset parameter

The output consists of dictionary with a key for each of the synthetic_sizes values.

For each synthetic_sizes value, the dictionary contains the keys:

• MIA_performance : accuracy and AUCROC for each attack
• MIA_scores: output scores for each attack
• data: the evaluation data

For both MIA_performance and MIA_scores, the following attacks are evaluated:

• "ablated_eq1" (Eq.1 (KDE))
• "ablated_eq2" (DOMIAS (KDE))
• "LOGAN_D1"
• "MC"
• "gan_leaks"
• "gan_leaks_cal"
• "LOGAN_0"
• "eq1" (Eq. 1 (BNAF))
• "domias"

Sample usage

Example for using evaluate_performance:

# third party
import pandas as pd
from sdv.tabular import TVAE

# domias absolute
from domias.evaluator import evaluate_performance
from domias.models.generator import GeneratorInterface


def get_generator(
    epochs: int = 1000,
    seed: int = 0,
) -> GeneratorInterface:
    class LocalGenerator(GeneratorInterface):
        def __init__(self) -> None:
            self.model = TVAE(epochs=epochs)

        def fit(self, data: pd.DataFrame) -> "LocalGenerator":
            self.model.fit(data)
            return self

        def generate(self, count: int) -> pd.DataFrame:
            return self.model.sample(count)

    return LocalGenerator()


dataset = ...  # Load your dataset as numpy array

mem_set_size = 1000
reference_set_size = 1000
training_epochs = 2000
synthetic_sizes = [1000]
density_estimator = "prior"  # prior, kde, bnaf

generator = get_generator(
    epochs=training_epochs,
)

perf = evaluate_performance(
    generator,
    dataset,
    mem_set_size,
    reference_set_size,
    training_epochs=training_epochs,
    synthetic_sizes=[100],
    density_estimator=density_estimator,
)

assert 100 in perf
results = perf[100]

assert "MIA_performance" in results
assert "MIA_scores" in results

print(results["MIA_performance"])

Experiments

1. Experiments main paper

To reproduce results for DOMIAS, baselines, and ablated models, run

cd experiments
python3 domias_main.py --seed 0 --gan_method TVAE --dataset housing --mem_set_size_list 30 50 100 300 500 1000 --reference_set_size_list 10000 --synthetic_sizes 10000 --training_epoch_list 2000

changing arguments mem_set_size_list, reference_set_size_list, synthetic_sizes, and training_epoch_list for specific experiments over ranges (Experiments 5.1 and 5.2, see Appendix A for details) and gan_method for generative model of interest.

or equivalently, run

cd experiments && bash run_tabular.sh

2. Experiments no prior knowledge (Appendix D)

If using prior knowledge (i.e., no reference dataset setting), add

--density_estimator prior

3. Experiment images (Appendix B.3)

Note: The CelebA dataset must be available in the experiments/data folder.

To run experiment with the CelebA dataset, first run

cd experiments && python3 celeba_gen.py --seed 0 --mem_set_size 4000

and then

cd experiments && python3 celeba_eval.py --seed 0 --mem_set_size 4000

Tests

Install the testing dependencies using

pip install .[testing]

The tests can be executed using

pytest -vsx

Citing

TODO