mindarmour.privacy.diff_privacy

This module provides Differential Privacy feature to protect user privacy.

class mindarmour.privacy.diff_privacy.AdaClippingWithGaussianRandom(decay_policy='Linear', learning_rate=0.001, target_unclipped_quantile=0.9, fraction_stddev=0.01, seed=0)

Adaptive clipping. If decay_policy is ‘Linear’, the update formula \(norm bound = norm bound - learning rate*(beta - target unclipped quantile)\). If decay_policy is ‘Geometric’, the update formula is \(norm bound = norm bound*exp(-learning rate*(empirical fraction - target unclipped quantile))\). where beta is the empirical fraction of samples with the value at most target_unclipped_quantile.

Parameters

• decay_policy (str) – Decay policy of adaptive clipping, decay_policy must be in [‘Linear’, ‘Geometric’]. Default: Linear.

• learning_rate (float) – Learning rate of update norm clip. Default: 0.001.

• target_unclipped_quantile (float) – Target quantile of norm clip. Default: 0.9.

• fraction_stddev (float) – The stddev of Gaussian normal which used in empirical_fraction, the formula is empirical_fraction + N(0, fraction_stddev). Default: 0.01.

• seed (int) – Original random seed, if seed=0 random normal will use secure random number. IF seed!=0 random normal will generate values using given seed. Default: 0.

Returns

Tensor, undated norm clip .

Examples

>>> decay_policy = 'Linear'
>>> beta = Tensor(0.5, mstype.float32)
>>> norm_bound = Tensor(1.0, mstype.float32)
>>> beta_stddev = 0.01
>>> learning_rate = 0.001
>>> target_unclipped_quantile = 0.9
>>> ada_clip = AdaClippingWithGaussianRandom(decay_policy=decay_policy,
>>>                                          learning_rate=learning_rate,
>>>                                          target_unclipped_quantile=target_unclipped_quantile,
>>>                                          fraction_stddev=beta_stddev)
>>> next_norm_bound = ada_clip(beta, norm_bound)

construct(empirical_fraction, norm_bound)

Update value of norm_bound.

Parameters

• empirical_fraction (Tensor) – empirical fraction of samples with the value at most target_unclipped_quantile.

• norm_bound (Tensor) – Clipping bound for the l2 norm of the gradients.

Returns

Tensor, generated noise with shape like given gradients.

class mindarmour.privacy.diff_privacy.ClipMechanismsFactory

Factory class of clip mechanisms

static create(mech_name, decay_policy='Linear', learning_rate=0.001, target_unclipped_quantile=0.9, fraction_stddev=0.01, seed=0)

Parameters

• mech_name (str) – Clip noise generated strategy, support ‘Gaussian’ now.

• decay_policy (str) – Decay policy of adaptive clipping, decay_policy must be in [‘Linear’, ‘Geometric’]. Default: Linear.

• learning_rate (float) – Learning rate of update norm clip. Default: 0.001.

• target_unclipped_quantile (float) – Target quantile of norm clip. Default: 0.9.

• fraction_stddev (float) – The stddev of Gaussian normal which used in empirical_fraction, the formula is \(empirical fraction + N(0, fraction sstddev)\). Default: 0.01.

• seed (int) – Original random seed, if seed=0 random normal will use secure random number. IF seed!=0 random normal will generate values using given seed. Default: 0.

Raises

NameError – mech_name must be in [‘Gaussian’].

Returns

Mechanisms, class of noise generated Mechanism.

Examples

>>> decay_policy = 'Linear'
>>> beta = Tensor(0.5, mstype.float32)
>>> norm_bound = Tensor(1.0, mstype.float32)
>>> beta_stddev = 0.1
>>> learning_rate = 0.1
>>> target_unclipped_quantile = 0.3
>>> clip_mechanism = ClipMechanismsFactory()
>>> ada_clip = clip_mechanism.create('Gaussian',
>>>                          decay_policy=decay_policy,
>>>                          learning_rate=learning_rate,
>>>                          target_unclipped_quantile=target_unclipped_quantile,
>>>                          fraction_stddev=beta_stddev)
>>> next_norm_bound = ada_clip(beta, norm_bound)

class mindarmour.privacy.diff_privacy.DPModel(micro_batches=2, norm_bound=1.0, noise_mech=None, clip_mech=None, **kwargs)

This class is overload mindspore.train.model.Model.

Parameters

• micro_batches (int) – The number of small batches split from an original batch. Default: 2.

• norm_bound (float) – Use to clip the bound, if set 1, will return the original data. Default: 1.0.

• noise_mech (Mechanisms) – The object can generate the different type of noise. Default: None.

• clip_mech (Mechanisms) – The object is used to update the adaptive clip. Default: None.

Raises

• ValueError – If DPOptimizer and noise_mecn are both None or not None.

• ValueError – If noise_mech or DPOtimizer’s mech method is adaptive while clip_mech is not None.

Examples

>>> norm_bound = 1.0
>>> initial_noise_multiplier = 0.01
>>> network = LeNet5()
>>> batch_size = 32
>>> batches = 128
>>> epochs = 1
>>> micro_batches = 2
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> factory_opt = DPOptimizerClassFactory(micro_batches=micro_batches)
>>> factory_opt.set_mechanisms('Gaussian',
>>>                            norm_bound=norm_bound,
>>>                            initial_noise_multiplier=initial_noise_multiplier)
>>> net_opt = factory_opt.create('Momentum')(network.trainable_params(),
>>>                                          learning_rate=0.1, momentum=0.9)
>>> clip_mech = ClipMechanismsFactory().create('Gaussian',
>>>                                            decay_policy='Linear',
>>>                                            learning_rate=0.01,
>>>                                            target_unclipped_quantile=0.9,
>>>                                            fraction_stddev=0.01)
>>> model = DPModel(micro_batches=micro_batches,
>>>                 norm_bound=norm_bound,
>>>                 clip_mech=clip_mech,
>>>                 noise_mech=None,
>>>                 network=network,
>>>                 loss_fn=loss,
>>>                 optimizer=net_opt,
>>>                 metrics=None)
>>> ms_ds = ds.GeneratorDataset(dataset_generator,
>>>                             ['data', 'label'])
>>> model.train(epochs, ms_ds, dataset_sink_mode=False)

class mindarmour.privacy.diff_privacy.DPOptimizerClassFactory(micro_batches=2)

Factory class of Optimizer.

Parameters

micro_batches (int) – The number of small batches split from an original batch. Default: 2.

Returns

Optimizer, Optimizer class.

Examples

>>> GaussianSGD = DPOptimizerClassFactory(micro_batches=2)
>>> GaussianSGD.set_mechanisms('Gaussian', norm_bound=1.0, initial_noise_multiplier=1.5)
>>> net_opt = GaussianSGD.create('Momentum')(params=network.trainable_params(),
>>>                                          learning_rate=0.001,
>>>                                          momentum=0.9)

create(policy)

Create DP optimizer. Policies can be ‘sgd’, ‘momentum’ or ‘adam’.

Parameters

policy (str) – Choose original optimizer type.

Returns

Optimizer, an optimizer with DP.

set_mechanisms(policy, *args, **kwargs)

Get noise mechanism object. Policies can be ‘sgd’, ‘momentum’ or ‘adam’. Candidate args and kwargs can be seen in class NoiseMechanismsFactory of mechanisms.py.

Parameters

policy (str) – Choose mechanism type.

class mindarmour.privacy.diff_privacy.NoiseAdaGaussianRandom(norm_bound=1.0, initial_noise_multiplier=1.0, seed=0, noise_decay_rate=6e-06, decay_policy='Exp')

Adaptive Gaussian noise generated mechanism. Noise would be decayed with training. Decay mode could be ‘Time’ mode, ‘Step’ mode, ‘Exp’ mode. self._noise_multiplier will be update during the model.train, using _MechanismsParamsUpdater.

Parameters

• 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: 1.0.

• seed (int) – Original random seed, if seed=0 random normal will use secure random number. IF seed!=0 random normal will generate values using given seed. Default: 0.

• noise_decay_rate (float) – Hyper parameter for controlling the noise decay. Default: 6e-6.

• decay_policy (str) – Noise decay strategy include ‘Step’, ‘Time’, ‘Exp’. Default: ‘Exp’.

Returns

Tensor, generated noise with shape like given gradients.

Examples

>>> gradients = Tensor([0.2, 0.9], mstype.float32)
>>> norm_bound = 1.0
>>> initial_noise_multiplier = 1.5
>>> seed = 0
>>> noise_decay_rate = 6e-4
>>> decay_policy = "Exp"
>>> net = NoiseAdaGaussianRandom(norm_bound, initial_noise_multiplier, seed, noise_decay_rate, decay_policy)
>>> res = net(gradients)
>>> print(res)

construct(gradients)

Generated Adaptive Gaussian noise.

Parameters

gradients (Tensor) – The gradients.

Returns

Tensor, generated noise with shape like given gradients.

class mindarmour.privacy.diff_privacy.NoiseGaussianRandom(norm_bound=1.0, initial_noise_multiplier=1.0, seed=0, decay_policy=None)

Gaussian noise generated mechanism.

Parameters

• 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: 1.0.

• seed (int) – Original random seed, if seed=0, random normal will use secure random number. If seed!=0, random normal will generate values using given seed. Default: 0.

• decay_policy (str) – Mechanisms parameters update policy. Default: None.

Returns

Tensor, generated noise with shape like given gradients.

Examples

>>> gradients = Tensor([0.2, 0.9], mstype.float32)
>>> norm_bound = 0.5
>>> initial_noise_multiplier = 1.5
>>> seed = 0
>>> decay_policy = None
>>> net = NoiseGaussianRandom(norm_bound, initial_noise_multiplier, seed, decay_policy)
>>> res = net(gradients)
>>> print(res)

construct(gradients)

Generated Gaussian noise.

Parameters

gradients (Tensor) – The gradients.

Returns

Tensor, generated noise with shape like given gradients.

class mindarmour.privacy.diff_privacy.NoiseMechanismsFactory

Factory class of noise mechanisms

static create(mech_name, norm_bound=1.0, initial_noise_multiplier=1.0, seed=0, noise_decay_rate=6e-06, decay_policy=None)

Parameters

• mech_name (str) – Noise generated strategy, could be ‘Gaussian’ or ‘AdaGaussian’. Noise would be decayed with ‘AdaGaussian’ mechanism while be constant with ‘Gaussian’ mechanism.

• 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: 1.0.

• seed (int) – Original random seed, if seed=0 random normal will use secure random number. IF seed!=0 random normal will generate values using given seed. Default: 0.

• noise_decay_rate (float) – Hyper parameter for controlling the noise decay. Default: 6e-6.

• decay_policy (str) – Mechanisms parameters update policy. Default: None, no parameters need update. Default: None.

Raises

NameError – mech_name must be in [‘Gaussian’, ‘AdaGaussian’].

Returns

Mechanisms, class of noise generated Mechanism.

Examples

>>> norm_bound = 1.0
>>> initial_noise_multiplier = 0.01
>>> network = LeNet5()
>>> batch_size = 32
>>> batches = 128
>>> epochs = 1
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> noise_mech = NoiseMechanismsFactory().create('Gaussian',
>>>                                              norm_bound=norm_bound,
>>>                                              initial_noise_multiplier=initial_noise_multiplier)
>>> clip_mech = ClipMechanismsFactory().create('Gaussian',
>>>                                            decay_policy='Linear',
>>>                                            learning_rate=0.01,
>>>                                            target_unclipped_quantile=0.9,
>>>                                            fraction_stddev=0.01)
>>> net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.1,
>>>                       momentum=0.9)
>>> model = DPModel(micro_batches=2,
>>>                 clip_mech=clip_mech,
>>>                 norm_bound=norm_bound,
>>>                 noise_mech=noise_mech,
>>>                 network=network,
>>>                 loss_fn=loss,
>>>                 optimizer=net_opt,
>>>                 metrics=None)
>>> ms_ds = ds.GeneratorDataset(dataset_generator,
>>>                            ['data', 'label'])
>>> model.train(epochs, ms_ds, dataset_sink_mode=False)

class mindarmour.privacy.diff_privacy.PrivacyMonitorFactory

Factory class of DP training’s privacy monitor.

static create(policy, *args, **kwargs)

Create a privacy monitor class.

Parameters

• policy (str) – Monitor policy, ‘rdp’ and ‘zcdp’ are supported by now. If policy is ‘rdp’, the monitor will compute the privacy budget of DP training based on Renyi differential privacy theory; If policy is ‘zcdp’, the monitor will compute the privacy budget of DP training based on zero-concentrated differential privacy theory. It’s worth noting that ‘zcdp’ is not suitable for subsampling nosie mechanism.

• args (Union[int, float, numpy.ndarray, list, str]) – Parameters used for creating a privacy monitor.

• kwargs (Union[int, float, numpy.ndarray, list, str]) – Keyword parameters used for creating a privacy monitor.

Returns

Callback, a privacy monitor.

Examples

>>> rdp = PrivacyMonitorFactory.create(policy='rdp',
>>> num_samples=60000, batch_size=32)

class mindarmour.privacy.diff_privacy.RDPMonitor(num_samples, batch_size, initial_noise_multiplier=1.5, max_eps=10.0, target_delta=0.001, max_delta=None, target_eps=None, orders=None, noise_decay_mode='Time', noise_decay_rate=0.0006, per_print_times=50, dataset_sink_mode=False)

Compute the privacy budget of DP training based on Renyi differential privacy (RDP) theory. According to the reference below, if a randomized mechanism is said to have ε’-Renyi differential privacy of order α, it also satisfies conventional differential privacy (ε, δ) as below:

\[(ε'+\frac{log(1/δ)}{α-1}, δ)\]

Reference: Rényi Differential Privacy of the Sampled Gaussian Mechanism

Parameters

• num_samples (int) – The total number of samples in training data sets.

• batch_size (int) – The number of samples in a batch while training.

• initial_noise_multiplier (Union[float, int]) – Ratio of the standard deviation of Gaussian noise divided by the norm_bound, which will be used to calculate privacy spent. Default: 1.5.

• max_eps (Union[float, int, None]) – The maximum acceptable epsilon budget for DP training, which is used for estimating the max training epochs. ‘None’ means there is no limit to epsilon budget. Default: 10.0.

• target_delta (Union[float, int, None]) – Target delta budget for DP training. If target_delta is set to be δ, then the privacy budget δ would be fixed during the whole training process. Default: 1e-3.

• max_delta (Union[float, int, None]) – The maximum acceptable delta budget for DP training, which is used for estimating the max training epochs. Max_delta must be less than 1 and suggested to be less than 1e-3, otherwise overflow would be encountered. ‘None’ means there is no limit to delta budget. Default: None.

• target_eps (Union[float, int, None]) – Target epsilon budget for DP training. If target_eps is set to be ε, then the privacy budget ε would be fixed during the whole training process. Default: None.

• orders (Union[None, list[int, float]]) – Finite orders used for computing rdp, which must be greater than 1. The computation result of privacy budget would be different for various orders. In order to obtain a tighter (smaller) privacy budget estimation, a list of orders could be tried. Default: None.

• noise_decay_mode (Union[None, str]) – Decay mode of adding noise while training, which can be None, ‘Time’, ‘Step’ or ‘Exp’. Default: ‘Time’.

• noise_decay_rate (float) – Decay rate of noise while training. Default: 6e-4.

• per_print_times (int) – The interval steps of computing and printing the privacy budget. Default: 50.

• dataset_sink_mode (bool) – If True, all training data would be passed to device(Ascend) one-time. If False, training data would be passed to device after each step training. Default: False.

Examples

>>> network = Net()
>>> net_loss = nn.SoftmaxCrossEntropyWithLogits()
>>> epochs = 2
>>> norm_clip = 1.0
>>> initial_noise_multiplier = 1.5
>>> mech = NoiseMechanismsFactory().create('AdaGaussian',
>>> norm_bound=norm_clip, initial_noise_multiplier=initial_noise_multiplier)
>>> net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
>>> model = DPModel(micro_batches=2, norm_clip=norm_clip,
>>> mech=mech, network=network, loss_fn=loss, optimizer=net_opt, metrics=None)
>>> rdp = PrivacyMonitorFactory.create(policy='rdp',
>>> num_samples=60000, batch_size=256,
>>> initial_noise_multiplier=initial_noise_multiplier)
>>> model.train(epochs, ds, callbacks=[rdp], dataset_sink_mode=False)

max_epoch_suggest()

Estimate the maximum training epochs to satisfy the predefined privacy budget.

Returns

int, the recommended maximum training epochs.

Examples

>>> rdp = PrivacyMonitorFactory.create(policy='rdp',
>>> num_samples=60000, batch_size=32)
>>> suggest_epoch = rdp.max_epoch_suggest()

step_end(run_context)

Compute privacy budget after each training step.

Parameters

run_context (RunContext) – Include some information of the model.

class mindarmour.privacy.diff_privacy.ZCDPMonitor(num_samples, batch_size, initial_noise_multiplier=1.5, max_eps=10.0, target_delta=0.001, noise_decay_mode='Time', noise_decay_rate=0.0006, per_print_times=50, dataset_sink_mode=False)

Compute the privacy budget of DP training based on zero-concentrated differential privacy theory (zcdp). According to the reference below, if a randomized mechanism is said to have ρ-ｚCDP, it also satisfies conventional differential privacy (ε, δ) as below:

\[(ρ+２\sqrt{ρlog(1/δ)}, δ)\]

It should be noted that ZCDPMonitor is not suitable for subsampling noise mechanisms(such as NoiseAdaGaussianRandom and NoiseGaussianRandom). The matching noise mechanism of ZCDP will be developed in the future. Reference: Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget

Parameters

• num_samples (int) – The total number of samples in training data sets.

• batch_size (int) – The number of samples in a batch while training.

• initial_noise_multiplier (Union[float, int]) – Ratio of the standard deviation of Gaussian noise divided by the norm_bound, which will be used to calculate privacy spent. Default: 1.5.

• max_eps (Union[float, int]) – The maximum acceptable epsilon budget for DP training, which is used for estimating the max training epochs. Default: 10.0.

• target_delta (Union[float, int]) – Target delta budget for DP training. If target_delta is set to be δ, then the privacy budget δ would be fixed during the whole training process. Default: 1e-3.

• noise_decay_mode (Union[None, str]) – Decay mode of adding noise while training, which can be None, ‘Time’, ‘Step’ or ‘Exp’. Default: ‘Time’.

• noise_decay_rate (float) – Decay rate of noise while training. Default: 6e-4.

• per_print_times (int) – The interval steps of computing and printing the privacy budget. Default: 50.

• dataset_sink_mode (bool) – If True, all training data would be passed to device(Ascend) one-time. If False, training data would be passed to device after each step training. Default: False.

Examples

>>> network = Net()
>>> net_loss = nn.SoftmaxCrossEntropyWithLogits()
>>> epochs = 2
>>> norm_clip = 1.0
>>> initial_noise_multiplier = 1.5
>>> mech = NoiseMechanismsFactory().create('AdaGaussian',
>>> norm_bound=norm_clip, initial_noise_multiplier=initial_noise_multiplier)
>>> net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
>>> model = DPModel(micro_batches=2, norm_clip=norm_clip,
>>> mech=mech, network=network, loss_fn=loss, optimizer=net_opt, metrics=None)
>>> zcdp = PrivacyMonitorFactory.create(policy='zcdp',
>>> num_samples=60000, batch_size=256,
>>> initial_noise_multiplier=initial_noise_multiplier)
>>> model.train(epochs, ds, callbacks=[zcdp], dataset_sink_mode=False)

max_epoch_suggest()

Estimate the maximum training epochs to satisfy the predefined privacy budget.

Returns

int, the recommended maximum training epochs.

Examples

>>> zcdp = PrivacyMonitorFactory.create(policy='zcdp',
>>> num_samples=60000, batch_size=32)
>>> suggest_epoch = zcdp.max_epoch_suggest()

step_end(run_context)

Compute privacy budget after each training step.

Parameters

run_context (RunContext) – Include some information of the model.