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11_run_Fredrickson_attack.py
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11_run_Fredrickson_attack.py
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#import pandas library
import sys
from time import time
from traceback import print_tb
from numpy import argmax
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, plot_roc_curve, roc_curve, auc
import torch
import data
from utils import fix_randomness
from models import Covid19_MainTaskModel, Adults_MainTaskModel, Fivethirtyeight_MainTaskModel, GSS_MainTaskModel
import yaml
fix_randomness()
# read config file
with open("./config/config.yaml", "r") as ymlfile:
try:
cfg = yaml.safe_load(ymlfile)
except yaml.YAMLError as exc:
print(exc)
# Step 1: Load data from .csv file
problem = cfg["problem"]
datapath = cfg["dataset"][problem]["path_to_data"]
if problem == 'covid19':
X, y = data.covid19_load_data(datapath)
elif problem == 'adults':
X, y, scaler = data.adults_load_data(datapath)
elif problem == 'fivethirtyeight':
X, y, scaler = data.fivethirtyeight_load_data(datapath)
elif problem == 'gss':
X, y, scaler = data.gss_load_data(datapath)
else:
print("The problem was not supported!!!")
sys.exit()
# Load attack feature configuration file
path_to_cfg_target_feature = "./config/target_attributes_" + problem + ".yaml"
with open(path_to_cfg_target_feature, "r") as ymlfile:
try:
cfg_target_attribute = yaml.safe_load(ymlfile)
except yaml.YAMLError as exc:
print(exc)
# Step 2: Define target attribute index
target_attribute_index = cfg_target_attribute["target_attribute"]["target_attribute_id"]
possible_attack_features = cfg_target_attribute["target_attribute"]["target_attribute_ids"]
###########################################################
# Step 3: Prepare data for training and test attack model #
###########################################################
# Step 3.1: Shuffle data
from sklearn.utils import shuffle
sklearn_random_state = cfg["random"]["random_state_sklearn"]
X, y = shuffle(X, y, random_state=sklearn_random_state)
# Step 3.2: get remain dataset to use in attack (for both train and test)
propotion_to_train_attack = cfg["attack_model"][problem]["propotion_to_train_attack_model"]
X_attack = X[int(X.shape[0]*(1.0-propotion_to_train_attack)):,:]
y_main_task = y[int(X.shape[0]*(1.0-propotion_to_train_attack)):]
# test attack model on remain dataset
propotion_to_train_attack = cfg["attack_model"]["train_size"]
X_test_attack = X_attack[int(X_attack.shape[0]*propotion_to_train_attack):,:]
y_test_main_task = y_main_task[int(X_attack.shape[0]*propotion_to_train_attack):]
X_test_attack = torch.from_numpy(X_test_attack).type(torch.FloatTensor)
#########################################################
# use X_train_attack... to calculate prior maginal of sensitive attribute
########################################################
# NOTE: This attack assumes Adversary knows prior maginal of target attribute
prior = [0]*len(possible_attack_features)
count = 0
if cfg_target_attribute['target_attribute']['isBinary'] == False:
for x in X_test_attack:
x_before_nomarlized = scaler.inverse_transform(x.reshape(1, -1)).astype(np.int64)[0]
for v in possible_attack_features:
if x_before_nomarlized[target_attribute_index]==v:
prior[v]=prior[v]+1
count+=1
prior = np.array(prior)
prior = prior/count
else:
for x in X_attack:
for v in possible_attack_features:
if x[target_attribute_index]==v:
prior[v]=prior[v]+1
count+=1
prior = np.array(prior)
prior = prior/count
print("Prior maginal: ", prior)
print("Major target attribute: ", argmax(prior))
main_task_model = torch.load(cfg["target_model"][problem]["path_to_target_model"])
# create train dataset for attack model
main_task_model_output = []
main_task_model_output_with_noise = []
# Fredrickson's Attack
x_sensitive_attribute_true=[]
x_sensitive_attribute_pred=[]
# NOTE: This attack assumes Adversary can access to confusion matrix
cms = np.loadtxt('./models/' + problem + '_confusion_matrix.txt', dtype=int)
with torch.no_grad():
for i, x in enumerate(X_test_attack):
if cfg_target_attribute['target_attribute']['isBinary'] == False:
x_before_nomarlized = scaler.inverse_transform(x.reshape(1, -1)).astype(np.int64)[0]
x_before_nomarlized_copy = x_before_nomarlized # copy to loop all possible values of target attribute
x_sensitive_attribute_true.append(x_before_nomarlized[target_attribute_index])
else:
x_copy = x # copy to loop all possible values of target attribute
x_sensitive_attribute_true.append(int(x[target_attribute_index].item()))
maximum = -1
for v in possible_attack_features:
# replace sensitive attribute by v
if cfg_target_attribute['target_attribute']['isBinary'] == False:
x_before_nomarlized_copy[target_attribute_index] = v
x_after_normalized = torch.from_numpy(scaler.transform(x_before_nomarlized_copy.reshape(1, -1))[0]).to(torch.float)
x_v = x_after_normalized
else:
x_copy[target_attribute_index] = v
x_v = x_copy
x_v = torch.unsqueeze(x_v, 0)
target_outputs = main_task_model(x_v)
y_pred = np.argmax(target_outputs, axis=1)
y_true = int(y_test_main_task[i])
C_ytrue_ypred = cms[y_true][y_pred]/np.sum(cms, axis=1)[y_true]
p_v = prior[v] # xác suất possible sensitive attribute = v, gọi là maginal prior
# print(p_v)
if C_ytrue_ypred*p_v>maximum:
maximum = C_ytrue_ypred * p_v
x_pred_ = v
# print(maximum)
x_sensitive_attribute_pred.append(x_pred_)
# for debugging
# if i==2:
# break
testAcc = accuracy_score(x_sensitive_attribute_true, x_sensitive_attribute_pred)
print("+ Accuracy = ", testAcc)
if cfg_target_attribute['target_attribute']['isBinary'] == True:
testF1 = f1_score(x_sensitive_attribute_true, x_sensitive_attribute_pred)
testPrecision = precision_score(x_sensitive_attribute_true, x_sensitive_attribute_pred)
testRecall = recall_score(x_sensitive_attribute_true, x_sensitive_attribute_pred)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
cm = confusion_matrix(x_sensitive_attribute_true, x_sensitive_attribute_pred)
print("+ Confusion matrix:\n", cm)