Source code for synthetic_aia_mia.mia
"""Membership inference attack."""
import pandas as pd
import numpy as np
from ..fetch_data import Dataset
from .ml import MiaNN
[docs]
class Mia:
"""High level interfate for membership inference attack."""
def __init__(self):
self.loss_mia = MiaNN()
[docs]
def fit(self, data):
"""Fit mia.
:param data: Datatset with member and non member labeled.
:type data: fetch_data.Dataset
"""
self.loss_mia.fit(data)
[docs]
def predict(self, data):
"""Add membership status prediction.
:param data: Dataset with loss.
:type data: fetch_data.Dataset
:return: Dataset with predicted membership status.
:rtype: fetch_data.Dataset
"""
data = self.loss_mia.predict(data)
return data
[docs]
def random_fusion(train, test):
"""Sample as many data points from train as they are in test. Then shuffle the sample and test to obtain a new dataset with as many train examples than test.
:param train: Member dataset.
:type train: fetch_data.DataSet
:param test: Non member dataset.
:type test: fetch_data.DataSet"""
testl = test.load()
trainl = train.load().sample(n=len(testl),random_state=42,ignore_index=True)
trainl.insert(loc=0,column="member",value=np.ones(len(trainl)))
testl.insert(loc=0,column="member",value=np.zeros(len(testl)))
data = pd.concat([trainl,testl])
data = data.sample(frac=1,random_state=234,ignore_index=True)
out = Dataset()
out.update(data)
return out