Update TFPs dp_accounting dependency.

PiperOrigin-RevId: 456293476

Author: michaelreneer

tensorflow_privacy/privacy/analysis/BUILD

@@ -13,10 +13,7 @@ py_library(
     name = "compute_dp_sgd_privacy_lib",
     srcs = ["compute_dp_sgd_privacy_lib.py"],
     srcs_version = "PY3",
-    deps = [
-        "@com_google_differential_py//python/dp_accounting:dp_event",
-        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
-    ],
+    deps = ["@com_google_differential_py//python/dp_accounting"],
 )
 
 py_binary(
@@ -45,10 +42,7 @@ py_binary(
 py_library(
     name = "compute_noise_from_budget_lib",
     srcs = ["compute_noise_from_budget_lib.py"],
-    deps = [
-        "@com_google_differential_py//python/dp_accounting:dp_event",
-        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
-    ],
+    deps = ["@com_google_differential_py//python/dp_accounting"],
 )
 
 py_test(
@@ -67,11 +61,7 @@ py_library(
     srcs = ["rdp_accountant.py"],
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
-    deps = [
-        "@com_google_differential_py//python/dp_accounting:dp_event",
-        "@com_google_differential_py//python/dp_accounting:privacy_accountant",
-        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
-    ],
+    deps = ["@com_google_differential_py//python/dp_accounting"],
 )
 
 py_test(
@@ -119,9 +109,7 @@ py_test(
     python_version = "PY3",
     srcs_version = "PY3",
     deps = [
-        ":rdp_accountant",
         ":tree_aggregation_accountant",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
-        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )

tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py

@@ -17,18 +17,18 @@
 import math
 
 from absl import app
-
-from com_google_differential_py.python.dp_accounting import dp_event
-from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
+from com_google_differential_py.python.dp_accounting
 
 
 def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
   """Compute and print results of DP-SGD analysis."""
 
-  accountant = rdp_privacy_accountant.RdpAccountant(orders)
+  accountant = dp_accounting.RdpAccountant(orders)
 
-  event = dp_event.SelfComposedDpEvent(
-      dp_event.PoissonSampledDpEvent(q, dp_event.GaussianDpEvent(sigma)), steps)
+  event = dp_accounting.SelfComposedDpEvent(
+      dp_accounting.PoissonSampledDpEvent(q,
+                                          dp_accounting.GaussianDpEvent(sigma)),
+      steps)
 
   accountant.compose(event)
 

tensorflow_privacy/privacy/analysis/compute_noise_from_budget_lib.py

@@ -17,18 +17,18 @@
 import math
 
 from absl import app
+from com_google_differential_py.python.dp_accounting
 from scipy import optimize
 
-from com_google_differential_py.python.dp_accounting import dp_event
-from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
-
 
 def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
   """Compute and print results of DP-SGD analysis."""
 
-  accountant = rdp_privacy_accountant.RdpAccountant(orders)
-  event = dp_event.SelfComposedDpEvent(
-      dp_event.PoissonSampledDpEvent(q, dp_event.GaussianDpEvent(sigma)), steps)
+  accountant = dp_accounting.RdpAccountant(orders)
+  event = dp_accounting.SelfComposedDpEvent(
+      dp_accounting.PoissonSampledDpEvent(q,
+                                          dp_accounting.GaussianDpEvent(sigma)),
+      steps)
   accountant.compose(event)
   return accountant.get_epsilon_and_optimal_order(delta)
 

tensorflow_privacy/privacy/analysis/rdp_accountant.py

@@ -41,12 +41,9 @@
   eps, _, opt_order = rdp_accountant.get_privacy_spent(rdp, target_delta=delta)
 """
 
+from com_google_differential_py.python.dp_accounting
 import numpy as np
 
-from com_google_differential_py.python.dp_accounting import dp_event
-from com_google_differential_py.python.dp_accounting import privacy_accountant
-from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
-
 
 def _compute_rdp_from_event(orders, event, count):
   """Computes RDP from a DpEvent using RdpAccountant.
@@ -61,15 +58,14 @@ def _compute_rdp_from_event(orders, event, count):
   """
   orders_vec = np.atleast_1d(orders)
 
-  if isinstance(event, dp_event.SampledWithoutReplacementDpEvent):
-    neighboring_relation = privacy_accountant.NeighboringRelation.REPLACE_ONE
-  elif isinstance(event, dp_event.SingleEpochTreeAggregationDpEvent):
-    neighboring_relation = privacy_accountant.NeighboringRelation.REPLACE_SPECIAL
+  if isinstance(event, dp_accounting.SampledWithoutReplacementDpEvent):
+    neighboring_relation = dp_accounting.NeighboringRelation.REPLACE_ONE
+  elif isinstance(event, dp_accounting.SingleEpochTreeAggregationDpEvent):
+    neighboring_relation = dp_accounting.NeighboringRelation.REPLACE_SPECIAL
   else:
-    neighboring_relation = privacy_accountant.NeighboringRelation.ADD_OR_REMOVE_ONE
+    neighboring_relation = dp_accounting.NeighboringRelation.ADD_OR_REMOVE_ONE
 
-  accountant = rdp_privacy_accountant.RdpAccountant(orders_vec,
-                                                    neighboring_relation)
+  accountant = dp_accounting.RdpAccountant(orders_vec, neighboring_relation)
   accountant.compose(event, count)
   rdp = accountant._rdp  # pylint: disable=protected-access
 
@@ -96,8 +92,8 @@ def compute_rdp(q, noise_multiplier, steps, orders):
   Returns:
     The RDPs at all orders. Can be `np.inf`.
   """
-  event = dp_event.PoissonSampledDpEvent(
-      q, dp_event.GaussianDpEvent(noise_multiplier))
+  event = dp_accounting.PoissonSampledDpEvent(
+      q, dp_accounting.GaussianDpEvent(noise_multiplier))
 
   return _compute_rdp_from_event(orders, event, steps)
 
@@ -129,8 +125,8 @@ def compute_rdp_sample_without_replacement(q, noise_multiplier, steps, orders):
   Returns:
     The RDPs at all orders, can be np.inf.
   """
-  event = dp_event.SampledWithoutReplacementDpEvent(
-      1, q, dp_event.GaussianDpEvent(noise_multiplier))
+  event = dp_accounting.SampledWithoutReplacementDpEvent(
+      1, q, dp_accounting.GaussianDpEvent(noise_multiplier))
 
   return _compute_rdp_from_event(orders, event, steps)
 
@@ -195,7 +191,7 @@ def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
     raise ValueError(
         "Exactly one out of eps and delta must be None. (None is).")
 
-  accountant = rdp_privacy_accountant.RdpAccountant(orders)
+  accountant = dp_accounting.RdpAccountant(orders)
   accountant._rdp = rdp  # pylint: disable=protected-access
 
   if target_eps is not None:

tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py

@@ -14,13 +14,10 @@
 # ==============================================================================
 
 from absl.testing import parameterized
+from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
-
 from tensorflow_privacy.privacy.analysis import tree_aggregation_accountant
 
-from com_google_differential_py.python.dp_accounting import dp_event
-from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
-
 
 class TreeAggregationTest(tf.test.TestCase, parameterized.TestCase):
 
@@ -33,8 +30,7 @@ def test_compute_eps_tree(self, noise_multiplier, eps):
     steps_list, target_delta = 1600, 1e-6
     rdp = tree_aggregation_accountant.compute_rdp_tree_restart(
         noise_multiplier, steps_list, orders)
-    new_eps = rdp_privacy_accountant.compute_epsilon(orders, rdp,
-                                                     target_delta)[0]
+    new_eps = dp_accounting.compute_epsilon(orders, rdp, target_delta)[0]
     self.assertLess(new_eps, eps)
 
   @parameterized.named_parameters(
@@ -67,7 +63,7 @@ def test_compute_eps_tree_decreasing(self, steps_list):
     for noise_multiplier in [0.1 * x for x in range(1, 100, 5)]:
       rdp = tree_aggregation_accountant.compute_rdp_tree_restart(
           noise_multiplier, steps_list, orders)
-      eps = rdp_privacy_accountant.compute_epsilon(orders, rdp, target_delta)[0]
+      eps = dp_accounting.compute_epsilon(orders, rdp, target_delta)[0]
       self.assertLess(eps, prev_eps)
       prev_eps = eps
 
@@ -90,8 +86,9 @@ def test_no_tree_no_sampling(self, total_steps, noise_multiplier):
     orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
     tree_rdp = tree_aggregation_accountant.compute_rdp_tree_restart(
         noise_multiplier, [1] * total_steps, orders)
-    accountant = rdp_privacy_accountant.RdpAccountant(orders)
-    accountant.compose(dp_event.GaussianDpEvent(noise_multiplier), total_steps)
+    accountant = dp_accounting.RdpAccountant(orders)
+    accountant.compose(
+        dp_accounting.GaussianDpEvent(noise_multiplier), total_steps)
     rdp = accountant._rdp  # pylint: disable=protected-access
     self.assertAllClose(tree_rdp, rdp, rtol=1e-12)
 

tensorflow_privacy/privacy/dp_query/BUILD

@@ -36,7 +36,7 @@ py_library(
     deps = [
         ":discrete_gaussian_utils",
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -59,7 +59,7 @@ py_library(
     deps = [
         ":discrete_gaussian_utils",
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -82,7 +82,7 @@ py_library(
     deps = [
         ":dp_query",
         ":normalized_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -103,7 +103,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -125,7 +125,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -167,7 +167,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -194,7 +194,7 @@ py_library(
         ":dp_query",
         ":gaussian_query",
         ":quantile_estimator_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -274,7 +274,7 @@ py_library(
     deps = [
         ":dp_query",
         ":tree_aggregation",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 
@@ -286,7 +286,7 @@ py_library(
         ":distributed_discrete_gaussian_query",
         ":dp_query",
         ":gaussian_query",
-        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting",
     ],
 )
 

tensorflow_privacy/privacy/dp_query/discrete_gaussian_query.py

@@ -15,12 +15,11 @@
 
 import collections
 
+from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import discrete_gaussian_utils
 from tensorflow_privacy.privacy.dp_query import dp_query
 
-from com_google_differential_py.python.dp_accounting import dp_event
-
 
 class DiscreteGaussianSumQuery(dp_query.SumAggregationDPQuery):
   """Implements DPQuery for discrete Gaussian sum queries.
@@ -84,5 +83,5 @@ def add_noise(v):
       return tf.ensure_shape(noised_v, v.shape)
 
     result = tf.nest.map_structure(add_noise, sample_state)
-    event = dp_event.UnsupportedDpEvent()
+    event = dp_accounting.UnsupportedDpEvent()
     return result, global_state, event

tensorflow_privacy/privacy/dp_query/distributed_discrete_gaussian_query.py

@@ -15,12 +15,11 @@
 
 import collections
 
+from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import discrete_gaussian_utils
 from tensorflow_privacy.privacy.dp_query import dp_query
 
-from com_google_differential_py.python.dp_accounting import dp_event
-
 
 class DistributedDiscreteGaussianSumQuery(dp_query.SumAggregationDPQuery):
   """Implements DPQuery for discrete distributed Gaussian sum queries.
@@ -108,5 +107,5 @@ def preprocess_record(self, params, record):
   def get_noised_result(self, sample_state, global_state):
     # Note that by directly returning the aggregate, this assumes that there
     # will not be missing local noise shares during execution.
-    event = dp_event.UnsupportedDpEvent()
+    event = dp_accounting.UnsupportedDpEvent()
     return sample_state, global_state, event

tensorflow_privacy/privacy/dp_query/distributed_skellam_query.py

@@ -15,12 +15,11 @@
 
 import collections
 
+from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import dp_query
 from tensorflow_privacy.privacy.dp_query import normalized_query
 
-from com_google_differential_py.python.dp_accounting import dp_event
-
 
 class DistributedSkellamSumQuery(dp_query.SumAggregationDPQuery):
   """Implements DPQuery interface for discrete distributed sum queries.
@@ -127,7 +126,7 @@ def preprocess_record(self, params, record):
 
   def get_noised_result(self, sample_state, global_state):
     """The noise was already added locally, therefore just continue."""
-    event = dp_event.UnsupportedDpEvent()
+    event = dp_accounting.UnsupportedDpEvent()
     return sample_state, global_state, event
 
 

tensorflow_privacy/privacy/dp_query/gaussian_query.py

@@ -16,11 +16,10 @@
 import collections
 import distutils
 
+from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import dp_query
 
-from com_google_differential_py.python.dp_accounting import dp_event
-
 
 class GaussianSumQuery(dp_query.SumAggregationDPQuery):
   """Implements DPQuery interface for Gaussian sum queries.
@@ -94,6 +93,6 @@ def add_noise(v):
 
     result = tf.nest.map_structure(add_noise, sample_state)
     noise_multiplier = global_state.stddev / global_state.l2_norm_clip
-    event = dp_event.GaussianDpEvent(noise_multiplier)
+    event = dp_accounting.GaussianDpEvent(noise_multiplier)
 
     return result, global_state, event