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from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn import datasets
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import numpy as np
from concrete.ml.sklearn import SGDClassifier
import time
from concrete.ml.sklearn import NeuralNetClassifier
import hashlib
RANDOM_STATE = 6
np.random.seed(RANDOM_STATE) #2 #1
def gen_database():
rng = np.random.default_rng(42)
X, y = datasets.load_breast_cancer(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)
scaler = MinMaxScaler(feature_range=[-1, 1])
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
perm = rng.permutation(x_train.shape[0])
x_train = x_train[perm, ::]
y_train = y_train[perm]
return x_train, y_train, x_test, y_test
def gen_trigger_set():
X_trigger = np.random.random_sample((15, 30))
y_trigger = np.random.randint(0, 2, (15))
for i in range(15):
if y_trigger[i] == 1:
X_trigger[i, :15] = X_trigger[i, 15]
else:
X_trigger[i, 15:] = X_trigger[i, 15]
return X_trigger, y_trigger
def train_model(x_train, y_train):
parameters_range = (-1.0, 1.0)
model = SGDClassifier(
random_state=42,
max_iter=100,
fit_encrypted=True,
parameters_range=parameters_range,
penalty=None,
learning_rate="constant",
verbose=1)
# %%
model.fit(x_train, y_train, fhe="simulate")
return model
def watermark_model(model, X_trigger, y_trigger):
model.max_iter = 17
model.alpha = 1e-6
model.penalty = "l2"
model.warm_start = True
a = time.time()
model.fit(X_trigger, y_trigger, fhe="simulate")
print("Time :", time.time() - a)
return model
def evaluate(model, x_train, y_train, x_test, y_test, X_trigger, y_trigger):
print(f"Accuracy Train Set :{np.sum(model.predict(x_train) == y_train) / len(y_train)}")
print(f"Accuracy Test Set :{np.sum(model.predict(x_test) == y_test) / len(y_test)}")
print(f"Accuracy Trigger Set :{np.sum(model.predict(X_trigger) == y_trigger) / len(y_trigger)}")
def get_model_hash(model):
m = hashlib.sha256()
m.update(model.coef_)
m.hexdigest()
return m.hexdigest()
def get_trigger_hash(X_trigger, y_trigger):
y_trigger = y_trigger.reshape(-1, 1)
trigger_set = np.concatenate((X_trigger, y_trigger), axis=1)
m = hashlib.sha256()
m.update(trigger_set)
m.hexdigest()
return m.hexdigest()
def test():
# Gen data
x_train, y_train, x_test, y_test = gen_database()
np.save("x_train", x_train)
np.save("y_train", y_train)
np.save("x_test", x_test)
np.save("y_test", y_test)
X_trigger, y_trigger = gen_trigger_set()
np.save("x_trigger", X_trigger)
np.save("y_trigger", y_trigger)
X_trigger, y_trigger = np.load("x_trigger.npy"), np.load("y_trigger.npy")
model = train_model(x_train, y_train)
np.save("model_coef", model.coef_)
np.save("model_intercept", model.intercept_)
model.coef_ = np.load("model_coef.npy")
model.intercept_ = np.load("model_intercept.npy")
wat_model = watermark_model(model, X_trigger, y_trigger)
np.save("wat_model_coef", wat_model.coef_)
np.save("wat_model_intercept", wat_model.intercept_)
wat_model.coef_ = np.load("wat_model_coef.npy")
wat_model.intercept_ = np.load("wat_model_intercept.npy")
evaluate(wat_model, x_train, y_train, x_test, y_test, X_trigger, y_trigger)
# test()
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