import securexgboost as xgb
import os
from sklearn.datasets import dump_svmlight_file
username = "******"
HOME_DIR = os.path.dirname(os.path.realpath(__file__)) + "/../../"
sym_key_file = HOME_DIR + "demo/data/key_zeros.txt"
pub_key_file = HOME_DIR + "demo/data/userkeys/private_user_1.pem"
cert_file = HOME_DIR + "demo/data/usercrts/{0}.crt".format(username)
temp_name = HOME_DIR + "demo/data/temp_file.txt"
temp_enc_name = HOME_DIR + "demo/data/temp_file.txt.enc"
print("Init user parameters")
xgb.init_user(username, sym_key_file, pub_key_file, cert_file)
print("Creating enclave")
enclave = xgb.Enclave(HOME_DIR + "build/enclave/xgboost_enclave.signed")
# Remote Attestation
print("Remote attestation")
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
enclave.attest(verify=False)
print("Send private key to enclave")
enclave.add_key()
import securexgboost as xgb
import os
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR + "/../../../"
SYM_KEY_FILE = DIR + "/../../data/key_zeros.txt"
PRIVATE_KEY_FILE = HOME_DIR + "config/user1.pem"
CERT_FILE = HOME_DIR + "config/user1.crt"
username = "******"
xgb.init_user(username, SYM_KEY_FILE, PRIVATE_KEY_FILE, CERT_FILE)
print("Creating enclave")
enclave = xgb.Enclave(HOME_DIR + "build/enclave/xgboost_enclave.signed")
# Remote Attestation
print("Remote attestation")
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
enclave.attest()
print("Send private key to enclave")
enclave.add_key()
rabit_args = {
"DMLC_NUM_WORKER": os.environ.get("DMLC_NUM_WORKER"),
"DMLC_NUM_SERVER": os.environ.get("DMLC_NUM_SERVER"),
"DMLC_TRACKER_URI": os.environ.get("DMLC_TRACKER_URI"),
"DMLC_TRACKER_PORT": os.environ.get("DMLC_TRACKER_PORT"),
"DMLC_ROLE": os.environ.get("DMLC_ROLE"),
"DMLC_NODE_HOST": os.environ.get("DMLC_NODE_HOST")
log_verbosity=0)
# Remote Attestation
print("Remote attestation")
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
enclave.attest()
username1 = "user1"
train_enc_1 = HOME_DIR + "demo/python/multiclient-distributed/data/u1_train.enc"
KEY_FILE_1 = HOME_DIR + "demo/python/multiclient-distributed/data/key1.txt"
PRIVATE_KEY_FILE_1 = HOME_DIR + "config/user1.pem"
CERT_FILE_1 = HOME_DIR + "config/user1.crt"
xgb.init_user(username1, KEY_FILE_1, PRIVATE_KEY_FILE_1, CERT_FILE_1)
print("Send private key to enclave")
enclave.add_key()
username2 = "user2"
train_enc_2 = HOME_DIR + "demo/python/multiclient-distributed/data/u2_train.enc"
test_enc_2 = HOME_DIR + "demo/python/multiclient-distributed/data/u2_test.enc"
KEY_FILE_2 = HOME_DIR + "demo/python/multiclient-distributed/data/key2.txt"
PRIVATE_KEY_FILE_2 = HOME_DIR + "config/user2.pem"
CERT_FILE_2 = HOME_DIR + "config/user2.crt"
xgb.init_user(username2, KEY_FILE_2, PRIVATE_KEY_FILE_2, CERT_FILE_2)
print("Send private key to enclave")
enclave.add_key()
def run(channel_addr, sym_key_file, priv_key_file, cert_file):
xgb.init_user(username, sym_key_file, priv_key_file, cert_file)
# Remote attestation
print("Remote attestation")
enclave_reference = xgb.Enclave(addr=channel_addr)
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
enclave_reference.attest()
print("Report successfully verified")
print("Send private key to enclave")
enclave_reference.add_key()
print("Creating training matrix")
dtrain = xgb.DMatrix({username: HOME_DIR + "demo/python/remote-control/data/train.enc"})
if not dtrain:
print("Error creating dtrain")
return
print("dtrain: " + dtrain.handle.value.decode("utf-8"))
print("Creating test matrix")
dtest = xgb.DMatrix({username: HOME_DIR + "demo/python/remote-control/data/test.enc"})
if not dtest:
print("Error creating dtest")
return
print("dtest: " + dtest.handle.value.decode("utf-8"))
print("Beginning Training")
# Set training parameters
params = {
"tree_method": "hist",
"n_gpus": "0",
"objective": "binary:logistic",
"min_child_weight": "1",
"gamma": "0.1",
"max_depth": "3",
"verbosity": "0"
}
# Train and evaluate
num_rounds = 5
print("Training...")
booster = xgb.train(params, dtrain, num_rounds)
print("booster: " + booster.handle.value.decode("utf-8"))
booster.save_model(HOME_DIR + "demo/python/remote-control/client/modelfile.model")
# Get encrypted predictions
print("\nModel Predictions: ")
predictions, num_preds = booster.predict(dtest, decrypt=False)
# Decrypt predictions
print(booster.decrypt_predictions(predictions, num_preds))
# Get fscores of model
print("\nModel Feature Importance: ")
print(booster.get_fscore())
