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domias-0.0.5
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مشاهده بیشترتوضیحات
DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | domias-0.0.5 |
| نام | domias |
| نسخه کتابخانه | 0.0.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | vanderschaarlab |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://www.vanderschaar-lab.com/ |
| آدرس اینترنتی | https://pypi.org/project/domias/ |
| مجوز | MIT |
# DOMIAS: Membership Inference Attacks against Synthetic Data through Overfitting Detection
<div align="center">
[](https://github.com/vanderschaarlab/DOMIAS/actions/workflows/test.yml)
[](https://pypi.org/project/domias/)
[](https://github.com/vanderschaarlab/DOMIAS/blob/main/LICENSE)
[](https://www.python.org/downloads/release/python-370/)
[](https://www.vanderschaar-lab.com/)
</div>
## Installation
The library can be installed from PyPI using
```bash
$ pip install domias
```
or from source, using
```bash
$ pip install .
```
## API
The main API call is
```python
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`:
```python
# 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
```bash
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
```bash
cd experiments && bash run_tabular.sh
```
2. **Experiments no prior knowledge (Appendix D)**
If using prior knowledge (i.e., no reference dataset setting), add
```python
--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
```bash
cd experiments && python3 celeba_gen.py --seed 0 --mem_set_size 4000
```
and then
```bash
cd experiments && python3 celeba_eval.py --seed 0 --mem_set_size 4000
```
## Tests
Install the testing dependencies using
```bash
pip install .[testing]
```
The tests can be executed using
```bash
pytest -vsx
```
## Citing
TODO
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pandas |
| - | torch |
| - | scikit-learn |
| - | tensorboardX |
| - | geomloss |
| - | importlib-metadata |
| - | setuptools |
| - | pytest |
| - | pytest-cov |
| - | pre-commit |
| - | black |
| - | flake8 |
| - | jupyter |
| - | notebook |
| - | isort |
نحوه نصب
نصب پکیج whl domias-0.0.5:
pip install domias-0.0.5.whl
نصب پکیج tar.gz domias-0.0.5:
pip install domias-0.0.5.tar.gz