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encrypt_param

encrypt_param

Classes

EncryptParam (BaseParam)

Define encryption method that used in federated ml.

Parameters:

Name Type Description Default
method {'Paillier', 'IterativeAffine', 'RandomIterativeAffine'}

If method is 'Paillier', Paillier encryption will be used for federated ml. To use non-encryption version in HomoLR, set this to None. For detail of Paillier encryption, please check out the paper mentioned in README file.

'Paillier'
key_length int, default: 1024

Used to specify the length of key in this encryption method.

1024
Source code in federatedml/param/encrypt_param.py
class EncryptParam(BaseParam):
    """
    Define encryption method that used in federated ml.

    Parameters
    ----------
    method : {'Paillier', 'IterativeAffine', 'RandomIterativeAffine'}
        If method is 'Paillier', Paillier encryption will be used for federated ml.
        To use non-encryption version in HomoLR, set this to None.
        For detail of Paillier encryption, please check out the paper mentioned in README file.

    key_length : int, default: 1024
        Used to specify the length of key in this encryption method.

    """

    def __init__(self, method=consts.PAILLIER, key_length=1024):
        super(EncryptParam, self).__init__()
        self.method = method
        self.key_length = key_length

    def check(self):
        if self.method is not None and type(self.method).__name__ != "str":
            raise ValueError(
                "encrypt_param's method {} not supported, should be str type".format(
                    self.method))
        elif self.method is None:
            pass
        else:
            user_input = self.method.lower()
            if user_input == "paillier":
                self.method = consts.PAILLIER
            elif user_input == "iterativeaffine":
                self.method = consts.ITERATIVEAFFINE
            elif user_input == "randomiterativeaffine":
                self.method = consts.RANDOM_ITERATIVEAFFINE
            else:
                raise ValueError(
                    "encrypt_param's method {} not supported".format(user_input))

        if type(self.key_length).__name__ != "int":
            raise ValueError(
                "encrypt_param's key_length {} not supported, should be int type".format(self.key_length))
        elif self.key_length <= 0:
            raise ValueError(
                "encrypt_param's key_length must be greater or equal to 1")

        LOGGER.debug("Finish encrypt parameter check!")
        return True
__init__(self, method='Paillier', key_length=1024) special
Source code in federatedml/param/encrypt_param.py
def __init__(self, method=consts.PAILLIER, key_length=1024):
    super(EncryptParam, self).__init__()
    self.method = method
    self.key_length = key_length
check(self)
Source code in federatedml/param/encrypt_param.py
def check(self):
    if self.method is not None and type(self.method).__name__ != "str":
        raise ValueError(
            "encrypt_param's method {} not supported, should be str type".format(
                self.method))
    elif self.method is None:
        pass
    else:
        user_input = self.method.lower()
        if user_input == "paillier":
            self.method = consts.PAILLIER
        elif user_input == "iterativeaffine":
            self.method = consts.ITERATIVEAFFINE
        elif user_input == "randomiterativeaffine":
            self.method = consts.RANDOM_ITERATIVEAFFINE
        else:
            raise ValueError(
                "encrypt_param's method {} not supported".format(user_input))

    if type(self.key_length).__name__ != "int":
        raise ValueError(
            "encrypt_param's key_length {} not supported, should be int type".format(self.key_length))
    elif self.key_length <= 0:
        raise ValueError(
            "encrypt_param's key_length must be greater or equal to 1")

    LOGGER.debug("Finish encrypt parameter check!")
    return True

Last update: 2021-12-03
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