def yeom_attribute_inference(true_x,
true_y,
classifier,
membership,
features,
train_loss,
test_loss=None):
print('-' * 10 + 'YEOM\'S ATTRIBUTE INFERENCE' + '-' * 10 + '\n')
pred_membership_all = []
for feature in features:
orignial_attribute = np.copy(true_x[:, feature])
low_value, high_value, true_attribute_value = get_attribute_variations(
true_x, feature)
true_x[:, feature] = low_value
pred_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={'x': true_x}, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=pred_input_fn)
_, low_op = get_predictions(predictions)
low_op = low_op.astype('float32')
low_op = log_loss(true_y, low_op)
true_x[:, feature] = high_value
pred_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={'x': true_x}, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=pred_input_fn)
_, high_op = get_predictions(predictions)
high_op = high_op.astype('float32')
high_op = log_loss(true_y, high_op)
high_prob = np.sum(true_attribute_value) / len(true_attribute_value)
low_prob = 1 - high_prob
if test_loss == None:
pred_attribute_value = np.where(
low_prob * stats.norm(0, train_loss).pdf(low_op) >=
high_prob * stats.norm(0, train_loss).pdf(high_op), 0, 1)
mask = [1] * len(pred_attribute_value)
else:
low_mem = np.where(
stats.norm(0, train_loss).pdf(low_op) >= stats.norm(
0, test_loss).pdf(low_op), 1, 0)
high_mem = np.where(
stats.norm(0, train_loss).pdf(high_op) >= stats.norm(
0, test_loss).pdf(high_op), 1, 0)
pred_attribute_value = [
np.argmax([low_prob * a, high_prob * b])
for a, b in zip(low_mem, high_mem)
]
mask = [a | b for a, b in zip(low_mem, high_mem)]
pred_membership = mask & (pred_attribute_value ^ true_attribute_value
^ [1] * len(pred_attribute_value))
prety_print_result(membership, pred_membership)
pred_membership_all.append(pred_membership)
true_x[:, feature] = orignial_attribute
return pred_membership_all
def proposed_attribute_inference(true_x, true_y, classifier, membership,
features, args):
print('-' * 10 + 'PROPOSED ATTRIBUTE INFERENCE' + '-' * 10 + '\n')
low_per_instance_loss_all, high_per_instance_loss_all = [], []
low_counts_all, high_counts_all = [], []
true_attribute_value_all = []
for feature in features:
orignial_attribute = np.copy(true_x[:, feature])
low_value, high_value, true_attribute_value = get_attribute_variations(
true_x, feature)
noise_params = (args.attack_noise_type, args.attack_noise_coverage,
args.attack_noise_magnitude)
true_x[:, feature] = low_value
pred_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={'x': true_x}, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=pred_input_fn)
_, low_op = get_predictions(predictions)
low_op = low_op.astype('float32')
low_op = log_loss(true_y, low_op)
low_counts = loss_increase_counts(true_x, true_y, classifier, low_op,
noise_params)
true_x[:, feature] = high_value
pred_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={'x': true_x}, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=pred_input_fn)
_, high_op = get_predictions(predictions)
high_op = high_op.astype('float32')
high_op = log_loss(true_y, high_op)
high_counts = loss_increase_counts(true_x, true_y, classifier, high_op,
noise_params)
true_attribute_value_all.append(true_attribute_value)
low_per_instance_loss_all.append(low_op)
high_per_instance_loss_all.append(high_op)
low_counts_all.append(low_counts)
high_counts_all.append(high_counts)
true_x[:, feature] = orignial_attribute
return (true_attribute_value_all, low_per_instance_loss_all,
high_per_instance_loss_all, low_counts_all, high_counts_all)