def run_experiment(args):
print('-' * 10 + 'TRAIN TARGET' + '-' * 10 + '\n')
dataset = load_data('target_data.npz', args)
v_dataset = load_data('shadow0_data.npz', args)
train_x, train_y, test_x, test_y = dataset
true_x = np.vstack((train_x, test_x))
true_y = np.append(train_y, test_y)
batch_size = args.target_batch_size
pred_y, membership, test_classes, classifier, aux = train_target_model(
args=args,
dataset=dataset,
epochs=args.target_epochs,
batch_size=args.target_batch_size,
learning_rate=args.target_learning_rate,
clipping_threshold=args.target_clipping_threshold,
n_hidden=args.target_n_hidden,
l2_ratio=args.target_l2_ratio,
model=args.target_model,
privacy=args.target_privacy,
dp=args.target_dp,
epsilon=args.target_epsilon,
delta=args.target_delta,
save=args.save_model)
train_loss, train_acc, test_loss, test_acc = aux
per_instance_loss = np.array(log_loss(true_y, pred_y))
# Yeom's membership inference attack when only train_loss is known
yeom_mi_outputs_1 = yeom_membership_inference(per_instance_loss, membership, train_loss)
# Yeom's membership inference attack when both train_loss and test_loss are known - Adversary 2 of Yeom et al.
yeom_mi_outputs_2 = yeom_membership_inference(per_instance_loss, membership, train_loss, test_loss)
# Proposed membership inference attacks
proposed_mi_outputs = proposed_membership_inference(v_dataset, true_x, true_y, classifier, per_instance_loss, args)
evaluate_proposed_membership_inference(per_instance_loss, membership, proposed_mi_outputs, fpr_threshold=0.01)
evaluate_proposed_membership_inference(per_instance_loss, membership, proposed_mi_outputs, fpr_threshold=0.01, per_class_thresh=True)
if not os.path.exists(RESULT_PATH+args.train_dataset):
os.makedirs(RESULT_PATH+args.train_dataset)
if args.target_privacy == 'no_privacy':
pickle.dump([aux, membership, per_instance_loss, yeom_mi_outputs_1, yeom_mi_outputs_2, proposed_mi_outputs], open(RESULT_PATH+args.train_dataset+'/'+str(args.target_test_train_ratio)+'_'+args.target_model+'_'+args.target_privacy+'_'+str(args.target_l2_ratio)+'_'+str(args.run)+'.p', 'wb'))
else:
pickle.dump([aux, membership, per_instance_loss, yeom_mi_outputs_1, yeom_mi_outputs_2, proposed_mi_outputs], open(RESULT_PATH+args.train_dataset+'/'+str(args.target_test_train_ratio)+'_'+args.target_model+'_'+args.target_privacy+'_'+args.target_dp+'_'+str(args.target_epsilon)+'_'+str(args.run)+'.p', 'wb'))
def run_experiment(args):
print('-' * 10 + 'TRAIN TARGET' + '-' * 10 + '\n')
dataset = load_data('target_data.npz', args)
train_x, train_y, test_x, test_y = dataset
true_x = np.vstack((train_x, test_x))
true_y = np.append(train_y, test_y)
batch_size = args.target_batch_size
pred_y, membership, test_classes, classifier, aux = train_target_model(
args=args,
dataset=dataset,
epochs=args.target_epochs,
batch_size=args.target_batch_size,
learning_rate=args.target_learning_rate,
clipping_threshold=args.target_clipping_threshold,
n_hidden=args.target_n_hidden,
l2_ratio=args.target_l2_ratio,
model=args.target_model,
privacy=args.target_privacy,
dp=args.target_dp,
epsilon=args.target_epsilon,
delta=args.target_delta,
save=args.save_model)
train_loss, train_acc, test_loss, test_acc = aux
per_instance_loss = np.array(log_loss(true_y, pred_y))
features = get_random_features(true_x, range(true_x.shape[1]), 5)
print(features)
# Yeom's membership inference attack when only train_loss is known
pred_membership = yeom_membership_inference(per_instance_loss, membership,
train_loss)
fpr, tpr, thresholds = roc_curve(membership, pred_membership, pos_label=1)
yeom_mem_adv = tpr[1] - fpr[1]
# Shokri's membership inference attack based on shadow model training
shokri_mem_adv, shokri_mem_confidence = shokri_membership_inference(
args, pred_y, membership, test_classes)
# Yeom's attribute inference attack when train_loss is known - Adversary 4 of Yeom et al.
pred_membership_all = yeom_attribute_inference(true_x, true_y, classifier,
membership, features,
train_loss)
yeom_attr_adv = []
for pred_membership in pred_membership_all:
fpr, tpr, thresholds = roc_curve(membership,
pred_membership,
pos_label=1)
yeom_attr_adv.append(tpr[1] - fpr[1])
if not os.path.exists(RESULT_PATH + args.train_dataset):
os.makedirs(RESULT_PATH + args.train_dataset)
if args.target_privacy == 'no_privacy':
pickle.dump(
[
train_acc, test_acc, train_loss, membership, shokri_mem_adv,
shokri_mem_confidence, yeom_mem_adv, per_instance_loss,
yeom_attr_adv, pred_membership_all, features
],
open(
RESULT_PATH + args.train_dataset + '/' + args.target_model +
'_' + 'no_privacy_' + str(args.l2_ratio) + '.p', 'wb'))
else:
pickle.dump(
[
train_acc, test_acc, train_loss, membership, shokri_mem_adv,
shokri_mem_confidence, yeom_mem_adv, per_instance_loss,
yeom_attr_adv, pred_membership_all, features
],
open(
RESULT_PATH + args.train_dataset + '/' + args.target_model +
'_' + args.target_privacy + '_' + args.target_dp + '_' +
str(args.target_epsilon) + '_' + str(args.run) + '.p', 'wb'))
#所有的数据都是提前分配处理好，然后保存到本地的硬盘当中。
#后续所有的训练操作都是从本地文件中读取数据，再进行训练。
dataset = load_data(args.DATA_PATH + 'target_data.npz')
print('Loading File From '+ args.DATA_PATH)
print '-' * 10 + 'TRAIN TARGET' + '-' * 10 + '\n'
attack_test_x, attack_test_y, test_classes = train_target_model(
dataset=dataset,
epochs=args.target_epochs,
batch_size=args.target_batch_size,
learning_rate=args.target_learning_rate,
n_hidden=args.target_n_hidden,
l2_ratio=args.target_l2_ratio,
model=args.target_model,
save=args.save_model,
DATA_PATH = args.DATA_PATH,
MODEL_PATH = args.MODEL_PATH)
print '-' * 10 + 'TRAIN SHADOW' + '-' * 10 + '\n'
attack_train_x, attack_train_y, train_classes = train_shadow_models(
epochs=args.target_epochs,
batch_size=args.target_batch_size,
learning_rate=args.target_learning_rate,
n_shadow=args.n_shadow,
n_hidden=args.target_n_hidden,
l2_ratio=args.target_l2_ratio,
model=args.target_model,