forked from 626_privacy/tensorflow_privacy
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
# 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.
|
|
# ==============================================================================
|
|
r"""Command-line script for computing privacy of a model trained with DP-SGD.
|
|
|
|
The script applies the RDP accountant to estimate privacy budget of an iterated
|
|
Sampled Gaussian Mechanism. The mechanism's parameters are controlled by flags.
|
|
|
|
Example:
|
|
compute_dp_sgd_privacy
|
|
--N=60000 \
|
|
--batch_size=256 \
|
|
--noise_multiplier=1.12 \
|
|
--epochs=60 \
|
|
--delta=1e-5
|
|
|
|
The output states that DP-SGD with these parameters satisfies (2.92, 1e-5)-DP.
|
|
"""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import math
|
|
|
|
from absl import app
|
|
from absl import flags
|
|
|
|
from privacy.analysis.rdp_accountant import compute_rdp
|
|
from privacy.analysis.rdp_accountant import get_privacy_spent
|
|
|
|
FLAGS = flags.FLAGS
|
|
|
|
flags.DEFINE_integer('N', None, 'Total number of examples')
|
|
flags.DEFINE_integer('batch_size', None, 'Batch size')
|
|
flags.DEFINE_float('noise_multiplier', None, 'Noise multiplier for DP-SGD')
|
|
flags.DEFINE_float('epochs', None, 'Number of epochs (may be fractional)')
|
|
flags.DEFINE_float('delta', 1e-6, 'Target delta')
|
|
|
|
flags.mark_flag_as_required('N')
|
|
flags.mark_flag_as_required('batch_size')
|
|
flags.mark_flag_as_required('noise_multiplier')
|
|
flags.mark_flag_as_required('epochs')
|
|
|
|
|
|
def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
|
|
"""Compute and print results of DP-SGD analysis."""
|
|
|
|
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.')
|
|
|
|
|
|
def main(argv):
|
|
del argv # argv is not used.
|
|
|
|
q = FLAGS.batch_size / FLAGS.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(FLAGS.epochs * FLAGS.N / FLAGS.batch_size))
|
|
|
|
apply_dp_sgd_analysis(q, FLAGS.noise_multiplier, steps, orders, FLAGS.delta)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
app.run(main)
|