Source code for mindarmour.diff_privacy.mechanisms.mechanisms

# Copyright 2019 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Noise Mechanisms.
"""
import numpy as np

from mindspore import Tensor
from mindspore.nn import Cell
from mindspore.ops import operations as P
from mindspore.common.parameter import Parameter
from mindspore.common import dtype as mstype

from mindarmour.utils._check_param import check_param_type
from mindarmour.utils._check_param import check_value_positive


[docs]class MechanismsFactory: """ Factory class of mechanisms""" def __init__(self): pass
[docs] @staticmethod def create(policy, *args, **kwargs): """ Args: policy(str): Noise generated strategy, could be 'Gaussian' or 'AdaGaussian'. Default: 'AdaGaussian'. args(Union[float, str]): Parameters used for creating noise mechanisms. kwargs(Union[float, str]): Parameters used for creating noise mechanisms. Raises: NameError: `policy` must be in ['Gaussian', 'AdaGaussian']. Returns: Mechanisms, class of noise generated Mechanism. """ if policy == 'Gaussian': return GaussianRandom(*args, **kwargs) if policy == 'AdaGaussian': return AdaGaussianRandom(*args, **kwargs) raise NameError("The {} is not implement, please choose " "['Gaussian', 'AdaGaussian']".format(policy))
class Mechanisms(Cell): """ Basic class of noise generated mechanism. """ def construct(self, shape): """ Construct function. """
[docs]class GaussianRandom(Mechanisms): """ Gaussian noise generated mechanism. Args: norm_bound(float): Clipping bound for the l2 norm of the gradients. Default: 1.0. initial_noise_multiplier(float): Ratio of the standard deviation of Gaussian noise divided by the norm_bound, which will be used to calculate privacy spent. Default: 1.5. Returns: Tensor, generated noise. Examples: >>> shape = (3, 2, 4) >>> norm_bound = 1.0 >>> initial_noise_multiplier = 1.5 >>> net = GaussianRandom(shape, norm_bound, initial_noise_multiplier) >>> res = net(shape) >>> print(res) """ def __init__(self, norm_bound=1.0, initial_noise_multiplier=1.5): super(GaussianRandom, self).__init__() self._norm_bound = check_value_positive('norm_bound', norm_bound) self._initial_noise_multiplier = check_value_positive('initial_noise_multiplier', initial_noise_multiplier,) stddev = self._norm_bound*self._initial_noise_multiplier self._stddev = stddev self._mean = 0
[docs] def construct(self, shape): """ Generated Gaussian noise. Args: shape(tuple): The shape of gradients. Returns: Tensor, generated noise. """ shape = check_param_type('shape', shape, tuple) noise = np.random.normal(self._mean, self._stddev, shape) return Tensor(noise, mstype.float32)
[docs]class AdaGaussianRandom(Mechanisms): """ Adaptive Gaussian noise generated mechanism. Args: norm_bound(float): Clipping bound for the l2 norm of the gradients. Default: 1.5. initial_noise_multiplier(float): Ratio of the standard deviation of Gaussian noise divided by the norm_bound, which will be used to calculate privacy spent. Default: 5.0. alpha(float): Hyperparameter for controlling the noise decay. Default: 6e-4. decay_policy(str): Noise decay strategy include 'Step' and 'Time'. Default: 'Time'. Returns: Tensor, generated noise. Examples: >>> shape = (3, 2, 4) >>> norm_bound = 1.0 >>> initial_noise_multiplier = 0.1 >>> alpha = 0.5 >>> decay_policy = "Time" >>> net = AdaGaussianRandom(norm_bound, initial_noise_multiplier, >>> alpha, decay_policy) >>> res = net(shape) >>> print(res) """ def __init__(self, norm_bound=1.5, initial_noise_multiplier=5.0, alpha=6e-4, decay_policy='Time'): super(AdaGaussianRandom, self).__init__() initial_noise_multiplier = check_value_positive('initial_noise_multiplier', initial_noise_multiplier) initial_noise_multiplier = Tensor(np.array(initial_noise_multiplier, np.float32)) self._initial_noise_multiplier = Parameter(initial_noise_multiplier, name='initial_noise_multiplier') self._noise_multiplier = Parameter(initial_noise_multiplier, name='noise_multiplier') norm_bound = check_value_positive('norm_bound', norm_bound) self._norm_bound = Tensor(np.array(norm_bound, np.float32)) alpha = check_param_type('alpha', alpha, float) self._alpha = Tensor(np.array(alpha, np.float32)) if decay_policy not in ['Time', 'Step']: raise NameError("The decay_policy must be in ['Time', 'Step'], but " "get {}".format(decay_policy)) self._decay_policy = decay_policy self._mean = 0.0 self._sub = P.Sub() self._mul = P.Mul() self._add = P.TensorAdd() self._div = P.Div() self._stddev = self._update_stddev() self._dtype = mstype.float32 def _update_multiplier(self): """ Update multiplier. """ if self._decay_policy == 'Time': temp = self._div(self._initial_noise_multiplier, self._noise_multiplier) temp = self._add(temp, self._alpha) temp = self._div(self._initial_noise_multiplier, temp) self._noise_multiplier = Parameter(temp, name='noise_multiplier') else: one = Tensor(1, self._dtype) temp = self._sub(one, self._alpha) temp = self._mul(temp, self._noise_multiplier) self._noise_multiplier = Parameter(temp, name='noise_multiplier') def _update_stddev(self): self._stddev = self._mul(self._noise_multiplier, self._norm_bound) return self._stddev
[docs] def construct(self, shape): """ Generate adaptive Gaussian noise. Args: shape(tuple): The shape of gradients. Returns: Tensor, generated noise. """ shape = check_param_type('shape', shape, tuple) noise = np.random.normal(self._mean, self._stddev.asnumpy(), shape) self._update_multiplier() self._update_stddev() return Tensor(noise, mstype.float32)