# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Library for computing privacy values for DP-SGD."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import sys
from absl import app
# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp # pylint: disable=g-import-not-at-top
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
"""Compute and print results of DP-SGD analysis."""
# compute_rdp requires that sigma be the ratio of the standard deviation of
# the Gaussian noise to the l2-sensitivity of the function to which it is
# added. Hence, sigma here corresponds to the `noise_multiplier` parameter
# in the DP-SGD implementation found in privacy.optimizers.dp_optimizer
rdp = compute_rdp(q, sigma, steps, orders)
eps, _, opt_order = get_privacy_spent(orders, rdp, target_delta=delta)
print('DP-SGD with sampling rate = {:.3g}% and noise_multiplier = {} iterated'
' over {} steps satisfies'.format(100 * q, sigma, steps), end=' ')
print('differential privacy with eps = {:.3g} and delta = {}.'.format(
eps, delta))
print('The optimal RDP order is {}.'.format(opt_order))
if opt_order == max(orders) or opt_order == min(orders):
print('The privacy estimate is likely to be improved by expanding '
'the set of orders.')
return eps, opt_order
def compute_dp_sgd_privacy(n, batch_size, noise_multiplier, epochs, delta):
"""Compute epsilon based on the given hyperparameters."""
q = batch_size / n # q - the sampling ratio.
if q > 1:
raise app.UsageError('n must be larger than the batch size.')
orders = ([1.25, 1.5, 1.75, 2., 2.25, 2.5, 3., 3.5, 4., 4.5] +
list(range(5, 64)) + [128, 256, 512])
steps = int(math.ceil(epochs * n / batch_size))
return apply_dp_sgd_analysis(q, noise_multiplier, steps, orders, delta)
