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.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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r"""Command-line script for computing privacy of a model trained with DP-SGD.
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The script applies the RDP accountant to estimate privacy budget of an iterated
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Sampled Gaussian Mechanism. The mechanism's parameters are controlled by flags.
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Example:
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compute_dp_sgd_privacy
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--N=60000 \
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--batch_size=256 \
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--noise_multiplier=1.12 \
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--epochs=60 \
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--delta=1e-5
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The output states that DP-SGD with these parameters satisfies (2.92, 1e-5)-DP.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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from absl import app
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from absl import flags
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from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
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from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
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FLAGS = flags.FLAGS
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flags.DEFINE_integer('N', None, 'Total number of examples')
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flags.DEFINE_integer('batch_size', None, 'Batch size')
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flags.DEFINE_float('noise_multiplier', None, 'Noise multiplier for DP-SGD')
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flags.DEFINE_float('epochs', None, 'Number of epochs (may be fractional)')
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flags.DEFINE_float('delta', 1e-6, 'Target delta')
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flags.mark_flag_as_required('N')
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flags.mark_flag_as_required('batch_size')
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flags.mark_flag_as_required('noise_multiplier')
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flags.mark_flag_as_required('epochs')
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def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
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"""Compute and print results of DP-SGD analysis."""
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rdp = compute_rdp(q, sigma, steps, orders)
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eps, _, opt_order = get_privacy_spent(orders, rdp, target_delta=delta)
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print('DP-SGD with sampling rate = {:.3g}% and noise_multiplier = {} iterated'
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' over {} steps satisfies'.format(100 * q, sigma, steps), end=' ')
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print('differential privacy with eps = {:.3g} and delta = {}.'.format(
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eps, delta))
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print('The optimal RDP order is {}.'.format(opt_order))
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if opt_order == max(orders) or opt_order == min(orders):
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print('The privacy estimate is likely to be improved by expanding '
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'the set of orders.')
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def main(argv):
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del argv # argv is not used.
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q = FLAGS.batch_size / FLAGS.N # q - the sampling ratio.
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if q > 1:
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raise app.UsageError('N must be larger than the batch size.')
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orders = ([1.25, 1.5, 1.75, 2., 2.25, 2.5, 3., 3.5, 4., 4.5] + range(5, 64) +
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[128, 256, 512])
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steps = int(math.ceil(FLAGS.epochs * FLAGS.N / FLAGS.batch_size))
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apply_dp_sgd_analysis(q, FLAGS.noise_multiplier, steps, orders, FLAGS.delta)
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if __name__ == '__main__':
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app.run(main)
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