noise mechanism of differential privacy

mindarmour/diff_privacy/__init__.py

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+"""
+This module provide Differential Privacy feature to protect user privacy.
+"""
+from .mechanisms.mechanisms import GaussianRandom
+from .mechanisms.mechanisms import AdaGaussianRandom
+from .mechanisms.mechanisms import MechanismsFactory
+
+__all__ = ['GaussianRandom',
+           'AdaGaussianRandom',
+           'MechanismsFactory']

mindarmour/diff_privacy/mechanisms/mechanisms.py

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+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+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
+
+
+class MechanismsFactory:
+    """ Factory class of mechanisms"""
+
+    def __init__(self):
+        pass
+
+    @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 __init__(self):
+        pass
+
+    def construct(self, shape):
+        """
+        Construct function.
+        """
+
+
+class GaussianRandom(Mechanisms):
+    """
+    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.
+
+    Returns:
+        Tensor, generated noise.
+
+    Examples:
+        >>> shape = (3, 2, 4)
+        >>> norm_bound = 1.5
+        >>> initial_noise_multiplier = 0.1
+        >>> net = GaussianRandom(shape, norm_bound, initial_noise_multiplier)
+        >>> res = net(shape)
+        >>> print(res)
+    """
+
+    def __init__(self, norm_bound=1.5, initial_noise_multiplier=5.0):
+        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
+
+    def construct(self, shape):
+        """
+        Generated Gaussian noise.
+
+        Args:
+            shape(tuple): The shape of gradients.
+
+        Returns:
+            numpy.ndarray, generated noise.
+        """
+        shape = check_param_type('shape', shape, tuple)
+        noise = np.random.normal(self._mean, self._stddev, shape)
+        return Tensor(noise, mstype.float32)
+
+
+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 = "Step"
+        >>> 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='Step'):
+        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))
+
+        self._decay_policy = check_param_type('decay_policy', decay_policy, str)
+        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
+
+    def construct(self, shape):
+        """
+        Generate adaptive Gaussian noise.
+
+        Args:
+            shape(tuple): The shape of gradients.
+
+        Returns:
+            numpy.ndarray, 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)

tests/ut/python/diff_privacy/mechanisms/test_mechanisms.py

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+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+different Privacy test.
+"""
+import pytest
+
+from mindspore import context
+from mindarmour.diff_privacy import GaussianRandom
+from mindarmour.diff_privacy import AdaGaussianRandom
+from mindarmour.diff_privacy import MechanismsFactory
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.component_mindarmour
+def test_gaussian():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    shape = (3, 2, 4)
+    norm_bound = 1.0
+    initial_noise_multiplier = 0.1
+    net = GaussianRandom(norm_bound, initial_noise_multiplier)
+    res = net(shape)
+    print(res)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.component_mindarmour
+def test_ada_gaussian():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    shape = (3, 2, 4)
+    norm_bound = 1.0
+    initial_noise_multiplier = 0.1
+    alpha = 0.5
+    decay_policy = "Step"
+    net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
+                            alpha, decay_policy)
+    res = net(shape)
+    print(res)
+
+
+def test_factory():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    shape = (3, 2, 4)
+    norm_bound = 1.0
+    initial_noise_multiplier = 0.1
+    alpha = 0.5
+    decay_policy = "Step"
+    noise_mechanism = MechanismsFactory()
+    noise_construct = noise_mechanism.create('Gaussian',
+                                             norm_bound,
+                                             initial_noise_multiplier)
+    noise = noise_construct(shape)
+    print('Gaussian noise: ', noise)
+    ada_mechanism = MechanismsFactory()
+    ada_noise_construct = ada_mechanism.create('AdaGaussian',
+                                               norm_bound,
+                                               initial_noise_multiplier,
+                                               alpha,
+                                               decay_policy)
+    ada_noise = ada_noise_construct(shape)
+    print('ada noise: ', ada_noise)
+
+
+if __name__ == '__main__':
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")