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Fix Issue #85: Support for cryptographically secure randomness #104

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Fix Issue #85: Support for cryptographically secure randomness #104

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f8ecfe8
Add cryptographically secure randomness support and asynchronous nois…
debanganghosh08 Jan 18, 2026
29ed405
Refactor: standalone secure noise example bypassing core API for arch…
debanganghosh08 Jan 21, 2026
6ab870e
Refactor: Use randomgen.Generator with AESCounter and streamline trai…
debanganghosh08 Jan 22, 2026
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125 changes: 125 additions & 0 deletions examples/secure_noise_example.py
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Original file line number Diff line number Diff line change
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# coding=utf-8
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@ryan112358 ryan112358 Jan 21, 2026

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Does this file pass pytype and pylint checks?

# Copyright 2025 DeepMind Technologies Limited.
#
# 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.

r"""Example of how to use cryptographically secure random noise generation.

This example demonstrates how to inject custom noise into the gradient noising
process, which is useful for sourcing randomness from outside of JAX's PRNG
framework (e.g., from a hardware security module).
"""

import time
from absl import app
from absl import flags
import jax
import jax.numpy as jnp
import numpy as np
from jax_privacy import noise_addition
from numpy.random import Generator
from randomgen import AESCounter

_USE_SECURE_RNG = flags.DEFINE_boolean(
'use_secure_rng', True, 'Whether to use secure random number generation.'
)
_STEPS = flags.DEFINE_integer(
'steps', 10, 'Number of training steps to run.'
)
_STDDEV = flags.DEFINE_float('stddev', 1.0, 'Noise standard deviation.')

def toy_model_params():
"""Returns a PyTree of model parameters for a toy model."""
return {
'layer1': jnp.zeros((1024, 1024)),
'layer2': jnp.zeros((1024, 512)),
}

def loss_fn(params, batch):
"""A dummy loss function."""
return sum(jnp.sum(p) for p in jax.tree.leaves(params))

# WARNING: This function must never be called inside a @jax.jit context.
# Doing so would cause the "random" noise to be statically compiled into the
# XLA graph, resulting in the same noise being added at every step.
def generate_secure_noise(stddev, params, generators):
"""Generates i.i.d. Gaussian noise on the CPU using randomgen."""
return jax.tree_util.tree_map(
lambda p, g: g.standard_normal(size=p.shape, dtype=p.dtype) * stddev,
params,
generators,
)

def main(_):
params = toy_model_params()
privatizer = noise_addition.gaussian_privatizer(
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@ryan112358 ryan112358 Jan 21, 2026

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Let's bypass the privatizer API all-together in this example, just directly add the noise using jax.tree.map(jnp.add, grads, noise)

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@debanganghosh08 debanganghosh08 Jan 22, 2026

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I have made the changes and also commented the technical detailed summery.

stddev=_STDDEV.value,
prng_key=jax.random.key(0),
)
privatizer_state = privatizer.init(params)

@jax.jit
def train_step(params, batch, privatizer_state, secure_noise):
"""
Computes gradients and adds noise.
If `secure_noise` is provided, it's used for noising. Otherwise, the
privatizer generates the noise.
"""
grads = jax.grad(loss_fn)(params, batch)

if secure_noise is not None:
noisy_grads = jax.tree_util.tree_map(jnp.add, grads, secure_noise)
# We still need to update the privatizer state to keep the PRNG key state
# consistent, even though we are not using its output.
_, new_privatizer_state = privatizer.update(grads, privatizer_state)
else:
noisy_grads, new_privatizer_state = privatizer.update(
grads, privatizer_state
)
return noisy_grads, new_privatizer_state

print(f"Running {_STEPS.value} steps with use_secure_rng={_USE_SECURE_RNG.value}")
start_time = time.time()

# Dummy batch
batch = None

# Create a pytree of generators, one for each parameter.
keys = jax.tree.map(
lambda p: np.random.randint(2**63, size=2, dtype=np.uint64),
params
)
generators = jax.tree.map(lambda k: Generator(AESCounter(k)), keys)

for step in range(_STEPS.value):
secure_noise_tree = None
if _USE_SECURE_RNG.value:
secure_noise_tree = generate_secure_noise(
_STDDEV.value, params, generators
)

_, privatizer_state = train_step(
params, batch, privatizer_state, secure_noise_tree
)

jax.block_until_ready(privatizer_state)
end_time = time.time()

total_time = end_time - start_time
avg_step_time = total_time / _STEPS.value

print(f"Total time for {_STEPS.value} steps: {total_time:.4f} seconds")
print(f"Average Step Time: {avg_step_time:.4f} seconds")

if __name__ == '__main__':
app.run(main)
3 changes: 2 additions & 1 deletion jax_privacy/noise_addition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -243,8 +243,9 @@ def privatize(sum_of_clipped_grads, noise_state):
rng_key=prng_key,
target_tree=target,
sampler=functools.partial(_gaussian_linear_combination, matrix_row),
dtype=dtype
dtype=dtype,
)

noisy_grads = jax.tree.map(strategy.add, sum_of_clipped_grads, noise)
return noisy_grads, index + 1

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