Restructure TF Privacy to be more in line with other repos in the TF ecosystem.

PiperOrigin-RevId: 274674077

Author: schien1729

tensorflow_privacy/__init__.py

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+# Copyright 2019, The TensorFlow Privacy Authors.
+#
+# 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.
+"""TensorFlow Privacy library."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+# pylint: disable=g-import-not-at-top
+
+if hasattr(sys, 'skip_tf_privacy_import'):  # Useful for standalone scripts.
+  pass
+else:
+  from tensorflow_privacy.privacy.analysis.privacy_ledger import GaussianSumQueryEntry
+  from tensorflow_privacy.privacy.analysis.privacy_ledger import PrivacyLedger
+  from tensorflow_privacy.privacy.analysis.privacy_ledger import QueryWithLedger
+  from tensorflow_privacy.privacy.analysis.privacy_ledger import SampleEntry
+
+  from tensorflow_privacy.privacy.dp_query.dp_query import DPQuery
+  from tensorflow_privacy.privacy.dp_query.gaussian_query import GaussianAverageQuery
+  from tensorflow_privacy.privacy.dp_query.gaussian_query import GaussianSumQuery
+  from tensorflow_privacy.privacy.dp_query.nested_query import NestedQuery
+  from tensorflow_privacy.privacy.dp_query.no_privacy_query import NoPrivacyAverageQuery
+  from tensorflow_privacy.privacy.dp_query.no_privacy_query import NoPrivacySumQuery
+  from tensorflow_privacy.privacy.dp_query.normalized_query import NormalizedQuery
+  from tensorflow_privacy.privacy.dp_query.quantile_adaptive_clip_sum_query import QuantileAdaptiveClipSumQuery
+  from tensorflow_privacy.privacy.dp_query.quantile_adaptive_clip_sum_query import QuantileAdaptiveClipAverageQuery
+
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradGaussianOptimizer
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradOptimizer
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamGaussianOptimizer
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamOptimizer
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
+  from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
+
+  try:
+    from tensorflow_privacy.privacy.bolt_on.models import BoltOnModel
+    from tensorflow_privacy.privacy.bolt_on.optimizers import BoltOn
+    from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
+    from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
+    from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexHuber
+  except ImportError:
+    # module `bolt_on` not yet available in this version of TF Privacy
+    pass

tensorflow_privacy/privacy/BUILD

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+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])
+
+exports_files(["LICENSE"])

tensorflow_privacy/privacy/__init__.py

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+# Copyright 2019, The TensorFlow Privacy Authors.
+#
+# 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.

tensorflow_privacy/privacy/analysis/__init__.py

@@ -0,0 +1,97 @@
+# 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
+import sys
+
+from absl import app
+from absl import flags
+
+# 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
+
+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."""
+
+  # 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.')
+
+
+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)