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"Client-server interface custom implementation for seizure detection models." |
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from concrete import fhe |
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from seizure_detection import SeizureDetector |
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class FHEServer: |
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"""Server interface to run a FHE circuit for seizure detection.""" |
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def __init__(self, model_path): |
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"""Initialize the FHE interface. |
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Args: |
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model_path (Path): The path to the directory where the circuit is saved. |
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""" |
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self.model_path = model_path |
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self.server = fhe.Server.load(self.model_path / "server.zip") |
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def run(self, serialized_encrypted_image, serialized_evaluation_keys): |
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"""Run seizure detection on the server over an encrypted image. |
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Args: |
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serialized_encrypted_image (bytes): The encrypted and serialized image. |
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serialized_evaluation_keys (bytes): The serialized evaluation keys. |
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Returns: |
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bytes: The encrypted boolean output indicating seizure detection. |
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""" |
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encrypted_image = fhe.Value.deserialize(serialized_encrypted_image) |
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evaluation_keys = fhe.EvaluationKeys.deserialize(serialized_evaluation_keys) |
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encrypted_output = self.server.run(encrypted_image, evaluation_keys=evaluation_keys) |
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serialized_encrypted_output = encrypted_output.serialize() |
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return serialized_encrypted_output |
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class FHEDev: |
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"""Development interface to save and load the seizure detection model.""" |
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def __init__(self, seizure_detector, model_path): |
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"""Initialize the FHE interface. |
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Args: |
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seizure_detector (SeizureDetector): The seizure detection model to use in the FHE interface. |
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model_path (str): The path to the directory where the circuit is saved. |
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""" |
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self.seizure_detector = seizure_detector |
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self.model_path = model_path |
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self.model_path.mkdir(parents=True, exist_ok=True) |
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def save(self): |
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"""Export all needed artifacts for the client and server interfaces.""" |
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assert self.seizure_detector.fhe_circuit is not None, ( |
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"The model must be compiled before saving it." |
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) |
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path_circuit_server = self.model_path / "server.zip" |
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self.seizure_detector.fhe_circuit.server.save(path_circuit_server, via_mlir=True) |
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path_circuit_client = self.model_path / "client.zip" |
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self.seizure_detector.fhe_circuit.client.save(path_circuit_client) |
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class FHEClient: |
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"""Client interface to encrypt and decrypt FHE data associated to a SeizureDetector.""" |
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def __init__(self, model_path, key_dir=None): |
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"""Initialize the FHE interface. |
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Args: |
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model_path (Path): The path to the directory where the circuit is saved. |
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key_dir (Path): The path to the directory where the keys are stored. Default to None. |
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""" |
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self.model_path = model_path |
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self.key_dir = key_dir |
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assert model_path.exists(), f"{model_path} does not exist. Please specify a valid path." |
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self.client = fhe.Client.load(self.model_path / "client.zip", self.key_dir) |
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self.seizure_detector = SeizureDetector() |
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def generate_private_and_evaluation_keys(self, force=False): |
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"""Generate the private and evaluation keys. |
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Args: |
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force (bool): If True, regenerate the keys even if they already exist. |
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""" |
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self.client.keygen(force) |
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def get_serialized_evaluation_keys(self): |
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"""Get the serialized evaluation keys. |
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Returns: |
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bytes: The evaluation keys. |
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""" |
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return self.client.evaluation_keys.serialize() |
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def encrypt_serialize(self, input_image): |
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"""Encrypt and serialize the input image in the clear. |
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Args: |
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input_image (numpy.ndarray): The image to encrypt and serialize. |
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Returns: |
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bytes: The pre-processed, encrypted and serialized image. |
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""" |
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encrypted_image = self.client.encrypt(input_image) |
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serialized_encrypted_image = encrypted_image.serialize() |
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return serialized_encrypted_image |
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def deserialize_decrypt_post_process(self, serialized_encrypted_output): |
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"""Deserialize, decrypt and post-process the output in the clear. |
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Args: |
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serialized_encrypted_output (bytes): The serialized and encrypted output. |
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Returns: |
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bool: The decrypted and deserialized boolean indicating seizure detection. |
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""" |
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encrypted_output = fhe.Value.deserialize(serialized_encrypted_output) |
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output = self.client.decrypt(encrypted_output) |
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seizure_detected = self.seizure_detector.post_processing(output) |
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return seizure_detected |
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