def set_task(self):
client = self.client
print(f"[+] {self.user_id} registering function")
function_id = client.register_function(
name="builtin-private-join-and-compute",
description="Native Private Join And Compute",
executor_type="builtin",
arguments=["num_user"],
inputs=[
FunctionInput("input_data0", "Bank A data file."),
FunctionInput("input_data1", "Bank B data file."),
FunctionInput("input_data2", "Bank C data file.")
],
outputs=[
FunctionOutput("output_data0", "Output data."),
FunctionOutput("output_data1", "Output data."),
FunctionOutput("output_data2", "Output date.")
])
print(f"[+] {self.user_id} creating task")
task_id = client.create_task(function_id=function_id,
function_arguments=({
"num_user": 3,
}),
executor="builtin",
inputs_ownership=[
OwnerList("input_data0",
[USER_DATA_0.user_id]),
OwnerList("input_data1",
[USER_DATA_1.user_id]),
OwnerList("input_data2",
[USER_DATA_2.user_id])
],
outputs_ownership=[
OwnerList("output_data0",
[USER_DATA_0.user_id]),
OwnerList("output_data1",
[USER_DATA_1.user_id]),
OwnerList("output_data2",
[USER_DATA_2.user_id])
])
return task_id
def set_task(self):
client = self.client
print(f"[+] {self.user_id} registering function")
function_id = client.register_function(
name="builtin-ordered-set-intersect",
description="Native Private Set Intersection",
executor_type="builtin",
arguments=["order"],
inputs=[
FunctionInput("input_data1", "Client 0 data."),
FunctionInput("input_data2", "Client 1 data.")
],
outputs=[
FunctionOutput("output_result1", "Output data."),
FunctionOutput("output_result2", "Output data.")
])
print(f"[+] {self.user_id} creating task")
task_id = client.create_task(
function_id=function_id,
function_arguments=({
"order": "ascending", # Order can be ascending or desending
}),
executor="builtin",
inputs_ownership=[
OwnerList("input_data1", [USER_DATA_0.user_id]),
OwnerList("input_data2", [USER_DATA_1.user_id])
],
outputs_ownership=[
OwnerList("output_result1", [USER_DATA_0.user_id]),
OwnerList("output_result2", [USER_DATA_1.user_id])
])
return task_id
def set_single_party_task(self,
functionname,
payloadpath,
args={},
inlabels=["input_data_0"],
outlabels=["result_data_0"],
ex=Executor.builtin):
client = self.client
print(f"[+] {self.user_id} registering function")
p_str = ""
if payloadpath != "":
print(f"[+] {self.user_id} reading payload file")
with open(payloadpath, "rb") as f:
p_str = f.read()
function_id = client.register_function(
name=functionname,
description="worker: %s" % functionname,
executor_type=ex.name,
arguments=list(args.keys()),
payload=list(p_str),
inputs=[
FunctionInput(label, "user input data file： %s" % label)
for label in inlabels
],
outputs=[
FunctionOutput(label, "user output file: %s" % label)
for label in outlabels
])
print(f"[+] {self.user_id} creating task")
task_id = client.create_task(function_id=function_id,
executor=ex.value,
function_arguments=(args),
inputs_ownership=[
OwnerList(label, [self.user_id])
for label in inlabels
],
outputs_ownership=[
OwnerList(label, [self.user_id])
for label in outlabels
])
return task_id
def gbdt(self):
client = AuthenticationService(
AUTHENTICATION_SERVICE_ADDRESS, AS_ROOT_CA_CERT_PATH,
ENCLAVE_INFO_PATH).connect().get_client()
print("[+] registering user")
client.user_register(self.user_id, self.user_password)
print("[+] login")
token = client.user_login(self.user_id, self.user_password)
client = FrontendService(FRONTEND_SERVICE_ADDRESS,
AS_ROOT_CA_CERT_PATH,
ENCLAVE_INFO_PATH).connect().get_client()
metadata = {"id": self.user_id, "token": token}
client.metadata = metadata
print("[+] registering function")
function_id = client.register_function(
name="builtin-gbdt-train",
description="Native Gbdt Training Function",
executor_type="builtin",
arguments=[
"feature_size", "max_depth", "iterations", "shrinkage",
"feature_sample_ratio", "data_sample_ratio", "min_leaf_size",
"loss", "training_optimization_level"
],
inputs=[
FunctionInput("training_data", "Input traning data file.")
],
outputs=[FunctionOutput("trained_model", "Output trained model.")])
print("[+] registering input file")
url = "http://localhost:6789/fixtures/functions/gbdt_training/train.enc"
cmac = "881adca6b0524472da0a9d0bb02b9af9"
schema = "teaclave-file-128"
key = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
iv = []
training_data_id = client.register_input_file(url, schema, key, iv,
cmac)
print("[+] registering output file")
url = "http://localhost:6789/fixtures/functions/gbdt_training/e2e_output_model.enc"
schema = "teaclave-file-128"
key = [
63, 195, 250, 208, 252, 127, 203, 27, 247, 168, 71, 77, 27, 47,
254, 240
]
iv = []
output_model_id = client.register_output_file(url, schema, key, iv)
print("[+] creating task")
task_id = client.create_task(
function_id=function_id,
function_arguments=({
"feature_size": 4,
"max_depth": 4,
"iterations": 100,
"shrinkage": 0.1,
"feature_sample_ratio": 1.0,
"data_sample_ratio": 1.0,
"min_leaf_size": 1,
"loss": "LAD",
"training_optimization_level": 2
}),
executor="builtin",
inputs_ownership=[OwnerList("training_data", [self.user_id])],
outputs_ownership=[OwnerList("trained_model", [self.user_id])])
print("[+] assigning data to task")
client.assign_data_to_task(
task_id, [DataMap("training_data", training_data_id)],
[DataMap("trained_model", output_model_id)])
print("[+] approving task")
client.approve_task(task_id)
print("[+] invoking task")
client.invoke_task(task_id)
print("[+] getting result")
result = client.get_task_result(task_id)
print("[+] done")
return bytes(result)