diff --git a/tensorflow_privacy/privacy/analysis/BUILD b/tensorflow_privacy/privacy/analysis/BUILD
index 39251e8..940baee 100644
--- a/tensorflow_privacy/privacy/analysis/BUILD
+++ b/tensorflow_privacy/privacy/analysis/BUILD
@@ -13,7 +13,10 @@ 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"],
+    deps = [
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
+    ],
 )
 
 py_binary(
@@ -42,7 +45,10 @@ 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"],
+    deps = [
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
+    ],
 )
 
 py_test(
@@ -61,7 +67,11 @@ py_library(
     srcs = ["rdp_accountant.py"],
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
-    deps = ["@com_google_differential_py//python/dp_accounting"],
+    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",
+    ],
 )
 
 py_test(
@@ -109,7 +119,9 @@ py_test(
     python_version = "PY3",
     srcs_version = "PY3",
     deps = [
+        ":rdp_accountant",
         ":tree_aggregation_accountant",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
diff --git a/tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py b/tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py
index 14e75a4..f884a32 100644
--- a/tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py
+++ b/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
+
+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 = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
 
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(q,
-                                          dp_accounting.GaussianDpEvent(sigma)),
-      steps)
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(q, dp_event.GaussianDpEvent(sigma)), steps)
 
   accountant.compose(event)
 
diff --git a/tensorflow_privacy/privacy/analysis/compute_noise_from_budget_lib.py b/tensorflow_privacy/privacy/analysis/compute_noise_from_budget_lib.py
index 4549e24..18214af 100644
--- a/tensorflow_privacy/privacy/analysis/compute_noise_from_budget_lib.py
+++ b/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 = dp_accounting.rdp.RdpAccountant(orders)
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(q,
-                                          dp_accounting.GaussianDpEvent(sigma)),
-      steps)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(q, dp_event.GaussianDpEvent(sigma)), steps)
   accountant.compose(event)
   return accountant.get_epsilon_and_optimal_order(delta)
 
diff --git a/tensorflow_privacy/privacy/analysis/rdp_accountant.py b/tensorflow_privacy/privacy/analysis/rdp_accountant.py
index 1344869..f35e442 100644
--- a/tensorflow_privacy/privacy/analysis/rdp_accountant.py
+++ b/tensorflow_privacy/privacy/analysis/rdp_accountant.py
@@ -41,9 +41,12 @@ The example code would be:
   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.
@@ -58,14 +61,15 @@ def _compute_rdp_from_event(orders, event, count):
   """
   orders_vec = np.atleast_1d(orders)
 
-  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
+  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
   else:
-    neighboring_relation = dp_accounting.NeighboringRelation.ADD_OR_REMOVE_ONE
+    neighboring_relation = privacy_accountant.NeighboringRelation.ADD_OR_REMOVE_ONE
 
-  accountant = dp_accounting.rdp.RdpAccountant(orders_vec, neighboring_relation)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders_vec,
+                                                    neighboring_relation)
   accountant.compose(event, count)
   rdp = accountant._rdp  # pylint: disable=protected-access
 
@@ -92,8 +96,8 @@ def compute_rdp(q, noise_multiplier, steps, orders):
   Returns:
     The RDPs at all orders. Can be `np.inf`.
   """
-  event = dp_accounting.PoissonSampledDpEvent(
-      q, dp_accounting.GaussianDpEvent(noise_multiplier))
+  event = dp_event.PoissonSampledDpEvent(
+      q, dp_event.GaussianDpEvent(noise_multiplier))
 
   return _compute_rdp_from_event(orders, event, steps)
 
@@ -125,8 +129,8 @@ def compute_rdp_sample_without_replacement(q, noise_multiplier, steps, orders):
   Returns:
     The RDPs at all orders, can be np.inf.
   """
-  event = dp_accounting.SampledWithoutReplacementDpEvent(
-      1, q, dp_accounting.GaussianDpEvent(noise_multiplier))
+  event = dp_event.SampledWithoutReplacementDpEvent(
+      1, q, dp_event.GaussianDpEvent(noise_multiplier))
 
   return _compute_rdp_from_event(orders, event, steps)
 
@@ -191,7 +195,7 @@ 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 = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
   accountant._rdp = rdp  # pylint: disable=protected-access
 
   if target_eps is not None:
diff --git a/tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py b/tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py
index 3403e0b..e94551c 100644
--- a/tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py
+++ b/tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py
@@ -14,10 +14,13 @@
 # ==============================================================================
 
 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):
 
@@ -30,7 +33,8 @@ class TreeAggregationTest(tf.test.TestCase, parameterized.TestCase):
     steps_list, target_delta = 1600, 1e-6
     rdp = tree_aggregation_accountant.compute_rdp_tree_restart(
         noise_multiplier, steps_list, orders)
-    new_eps = dp_accounting.rdp.compute_epsilon(orders, rdp, target_delta)[0]
+    new_eps = rdp_privacy_accountant.compute_epsilon(orders, rdp,
+                                                     target_delta)[0]
     self.assertLess(new_eps, eps)
 
   @parameterized.named_parameters(
@@ -63,7 +67,7 @@ class TreeAggregationTest(tf.test.TestCase, parameterized.TestCase):
     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 = dp_accounting.rdp.compute_epsilon(orders, rdp, target_delta)[0]
+      eps = rdp_privacy_accountant.compute_epsilon(orders, rdp, target_delta)[0]
       self.assertLess(eps, prev_eps)
       prev_eps = eps
 
@@ -86,9 +90,8 @@ class TreeAggregationTest(tf.test.TestCase, parameterized.TestCase):
     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 = dp_accounting.rdp.RdpAccountant(orders)
-    accountant.compose(
-        dp_accounting.GaussianDpEvent(noise_multiplier), total_steps)
+    accountant = rdp_privacy_accountant.RdpAccountant(orders)
+    accountant.compose(dp_event.GaussianDpEvent(noise_multiplier), total_steps)
     rdp = accountant._rdp  # pylint: disable=protected-access
     self.assertAllClose(tree_rdp, rdp, rtol=1e-12)
 
diff --git a/tensorflow_privacy/privacy/dp_query/BUILD b/tensorflow_privacy/privacy/dp_query/BUILD
index c385dde..0bbafd8 100644
--- a/tensorflow_privacy/privacy/dp_query/BUILD
+++ b/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",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -59,7 +59,7 @@ py_library(
     deps = [
         ":discrete_gaussian_utils",
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -82,7 +82,7 @@ py_library(
     deps = [
         ":dp_query",
         ":normalized_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -103,7 +103,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -125,7 +125,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -167,7 +167,7 @@ py_library(
     srcs_version = "PY3",
     deps = [
         ":dp_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -194,7 +194,7 @@ py_library(
         ":dp_query",
         ":gaussian_query",
         ":quantile_estimator_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -274,7 +274,7 @@ py_library(
     deps = [
         ":dp_query",
         ":tree_aggregation",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
@@ -286,7 +286,7 @@ py_library(
         ":distributed_discrete_gaussian_query",
         ":dp_query",
         ":gaussian_query",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
     ],
 )
 
diff --git a/tensorflow_privacy/privacy/dp_query/discrete_gaussian_query.py b/tensorflow_privacy/privacy/dp_query/discrete_gaussian_query.py
index 6870124..b151b68 100644
--- a/tensorflow_privacy/privacy/dp_query/discrete_gaussian_query.py
+++ b/tensorflow_privacy/privacy/dp_query/discrete_gaussian_query.py
@@ -15,11 +15,12 @@
 
 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.
@@ -83,5 +84,5 @@ class DiscreteGaussianSumQuery(dp_query.SumAggregationDPQuery):
       return tf.ensure_shape(noised_v, v.shape)
 
     result = tf.nest.map_structure(add_noise, sample_state)
-    event = dp_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return result, global_state, event
diff --git a/tensorflow_privacy/privacy/dp_query/distributed_discrete_gaussian_query.py b/tensorflow_privacy/privacy/dp_query/distributed_discrete_gaussian_query.py
index 3b4e6b5..514f297 100644
--- a/tensorflow_privacy/privacy/dp_query/distributed_discrete_gaussian_query.py
+++ b/tensorflow_privacy/privacy/dp_query/distributed_discrete_gaussian_query.py
@@ -15,11 +15,12 @@
 
 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.
@@ -107,5 +108,5 @@ class DistributedDiscreteGaussianSumQuery(dp_query.SumAggregationDPQuery):
   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_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return sample_state, global_state, event
diff --git a/tensorflow_privacy/privacy/dp_query/distributed_skellam_query.py b/tensorflow_privacy/privacy/dp_query/distributed_skellam_query.py
index 971499a..e721e82 100644
--- a/tensorflow_privacy/privacy/dp_query/distributed_skellam_query.py
+++ b/tensorflow_privacy/privacy/dp_query/distributed_skellam_query.py
@@ -15,11 +15,12 @@
 
 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.
@@ -126,7 +127,7 @@ class DistributedSkellamSumQuery(dp_query.SumAggregationDPQuery):
 
   def get_noised_result(self, sample_state, global_state):
     """The noise was already added locally, therefore just continue."""
-    event = dp_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return sample_state, global_state, event
 
 
diff --git a/tensorflow_privacy/privacy/dp_query/gaussian_query.py b/tensorflow_privacy/privacy/dp_query/gaussian_query.py
index 966fd72..bc5c4ef 100644
--- a/tensorflow_privacy/privacy/dp_query/gaussian_query.py
+++ b/tensorflow_privacy/privacy/dp_query/gaussian_query.py
@@ -16,10 +16,11 @@
 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.
@@ -93,6 +94,6 @@ class GaussianSumQuery(dp_query.SumAggregationDPQuery):
 
     result = tf.nest.map_structure(add_noise, sample_state)
     noise_multiplier = global_state.stddev / global_state.l2_norm_clip
-    event = dp_accounting.GaussianDpEvent(noise_multiplier)
+    event = dp_event.GaussianDpEvent(noise_multiplier)
 
     return result, global_state, event
diff --git a/tensorflow_privacy/privacy/dp_query/nested_query.py b/tensorflow_privacy/privacy/dp_query/nested_query.py
index 45d5df2..da1b808 100644
--- a/tensorflow_privacy/privacy/dp_query/nested_query.py
+++ b/tensorflow_privacy/privacy/dp_query/nested_query.py
@@ -15,11 +15,12 @@
 
 import collections
 
-from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import dp_query
 import tree
 
+from com_google_differential_py.python.dp_accounting import dp_event
+
 
 class NestedQuery(dp_query.DPQuery):
   """Implements DPQuery interface for structured queries.
@@ -101,7 +102,7 @@ class NestedQuery(dp_query.DPQuery):
 
     return (tf.nest.pack_sequence_as(self._queries, flat_estimates),
             tf.nest.pack_sequence_as(self._queries, flat_new_global_states),
-            dp_accounting.ComposedDpEvent(events=flat_events))
+            dp_event.ComposedDpEvent(events=flat_events))
 
   def derive_metrics(self, global_state):
     """Implements `tensorflow_privacy.DPQuery.derive_metrics`."""
diff --git a/tensorflow_privacy/privacy/dp_query/no_privacy_query.py b/tensorflow_privacy/privacy/dp_query/no_privacy_query.py
index 189926d..5ca70bc 100644
--- a/tensorflow_privacy/privacy/dp_query/no_privacy_query.py
+++ b/tensorflow_privacy/privacy/dp_query/no_privacy_query.py
@@ -13,10 +13,11 @@
 # limitations under the License.
 """Implements DPQuery interface for no privacy average queries."""
 
-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 NoPrivacySumQuery(dp_query.SumAggregationDPQuery):
   """Implements DPQuery interface for a sum query with no privacy.
@@ -26,7 +27,7 @@ class NoPrivacySumQuery(dp_query.SumAggregationDPQuery):
 
   def get_noised_result(self, sample_state, global_state):
     """Implements `tensorflow_privacy.DPQuery.get_noised_result`."""
-    return sample_state, global_state, dp_accounting.NonPrivateDpEvent()
+    return sample_state, global_state, dp_event.NonPrivateDpEvent()
 
 
 class NoPrivacyAverageQuery(dp_query.SumAggregationDPQuery):
@@ -85,4 +86,4 @@ class NoPrivacyAverageQuery(dp_query.SumAggregationDPQuery):
     sum_state, denominator = sample_state
 
     result = tf.nest.map_structure(lambda t: t / denominator, sum_state)
-    return result, global_state, dp_accounting.NonPrivateDpEvent()
+    return result, global_state, dp_event.NonPrivateDpEvent()
diff --git a/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py
index 0bf5569..6fd9a18 100644
--- a/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py
+++ b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py
@@ -15,12 +15,13 @@
 
 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 gaussian_query
 from tensorflow_privacy.privacy.dp_query import quantile_estimator_query
 
+from com_google_differential_py.python.dp_accounting import dp_event
+
 
 class QuantileAdaptiveClipSumQuery(dp_query.SumAggregationDPQuery):
   """`DPQuery` for Gaussian sum queries with adaptive clipping.
@@ -137,7 +138,7 @@ class QuantileAdaptiveClipSumQuery(dp_query.SumAggregationDPQuery):
                                          new_sum_query_state,
                                          new_quantile_estimator_state)
 
-    event = dp_accounting.ComposedDpEvent(events=[sum_event, quantile_event])
+    event = dp_event.ComposedDpEvent(events=[sum_event, quantile_event])
     return noised_vectors, new_global_state, event
 
   def derive_metrics(self, global_state):
diff --git a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py
index 671339a..e71bc06 100644
--- a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py
+++ b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py
@@ -34,11 +34,11 @@ corresponding epsilon for a `target_delta` and `noise_multiplier` to achieve
 """
 
 import attr
-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 tree_aggregation
 
+from com_google_differential_py.python.dp_accounting import dp_event
 
 # TODO(b/193679963): define `RestartQuery` and move `RestartIndicator` to be
 # in the same module.
@@ -186,7 +186,7 @@ class TreeCumulativeSumQuery(dp_query.SumAggregationDPQuery):
         global_state,
         samples_cumulative_sum=new_cumulative_sum,
         tree_state=new_tree_state)
-    event = dp_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return noised_cumulative_sum, new_global_state, event
 
   def reset_state(self, noised_results, global_state):
@@ -428,7 +428,7 @@ class TreeResidualSumQuery(dp_query.SumAggregationDPQuery):
                                           global_state.previous_tree_noise)
     new_global_state = attr.evolve(
         global_state, previous_tree_noise=tree_noise, tree_state=new_tree_state)
-    event = dp_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return noised_sample, new_global_state, event
 
   def reset_state(self, noised_results, global_state):
diff --git a/tensorflow_privacy/privacy/dp_query/tree_range_query.py b/tensorflow_privacy/privacy/dp_query/tree_range_query.py
index de0e1b2..b3a603b 100644
--- a/tensorflow_privacy/privacy/dp_query/tree_range_query.py
+++ b/tensorflow_privacy/privacy/dp_query/tree_range_query.py
@@ -21,12 +21,13 @@ import math
 from typing import Optional
 
 import attr
-from com_google_differential_py.python.dp_accounting
 import tensorflow as tf
 from tensorflow_privacy.privacy.dp_query import distributed_discrete_gaussian_query
 from tensorflow_privacy.privacy.dp_query import dp_query
 from tensorflow_privacy.privacy.dp_query import gaussian_query
 
+from com_google_differential_py.python.dp_accounting import dp_event
+
 
 def _build_tree_from_leaf(leaf_nodes: tf.Tensor, arity: int) -> tf.RaggedTensor:
   """A function constructs a complete tree given all the leaf nodes.
@@ -203,7 +204,7 @@ class TreeRangeSumQuery(dp_query.SumAggregationDPQuery):
     ]
     tree = tf.RaggedTensor.from_row_splits(
         values=sample_state, row_splits=row_splits)
-    event = dp_accounting.UnsupportedDpEvent()
+    event = dp_event.UnsupportedDpEvent()
     return tree, new_global_state, event
 
   @classmethod
diff --git a/tensorflow_privacy/privacy/logistic_regression/BUILD b/tensorflow_privacy/privacy/logistic_regression/BUILD
index 18f59aa..c1bc1ce 100644
--- a/tensorflow_privacy/privacy/logistic_regression/BUILD
+++ b/tensorflow_privacy/privacy/logistic_regression/BUILD
@@ -12,7 +12,9 @@ py_library(
         ":datasets",
         ":single_layer_softmax",
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer_keras",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting:mechanism_calibration",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
diff --git a/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py b/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
index 4d93f38..a4ab2a0 100644
--- a/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
+++ b/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
@@ -28,13 +28,16 @@ the algorithm of Abadi et al.: https://arxiv.org/pdf/1607.00133.pdf%20.
 import math
 from typing import List, Optional, Tuple
 
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
 from tensorflow_privacy.privacy.logistic_regression import datasets
 from tensorflow_privacy.privacy.logistic_regression import single_layer_softmax
 from tensorflow_privacy.privacy.optimizers import dp_optimizer_keras
 
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting import mechanism_calibration
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
+
 
 @tf.keras.utils.register_keras_serializable(package='Custom', name='Kifer')
 class KiferRegularizer(tf.keras.regularizers.Regularizer):
@@ -173,17 +176,17 @@ def compute_dpsgd_noise_multiplier(num_train: int,
   steps = int(math.ceil(epochs * num_train / batch_size))
 
   def make_event_from_param(noise_multiplier):
-    return dp_accounting.SelfComposedDpEvent(
-        dp_accounting.PoissonSampledDpEvent(
+    return dp_event.SelfComposedDpEvent(
+        dp_event.PoissonSampledDpEvent(
             sampling_probability=batch_size / num_train,
-            event=dp_accounting.GaussianDpEvent(noise_multiplier)), steps)
+            event=dp_event.GaussianDpEvent(noise_multiplier)), steps)
 
-  return dp_accounting.calibrate_dp_mechanism(
-      lambda: dp_accounting.rdp.RdpAccountant(orders),
+  return mechanism_calibration.calibrate_dp_mechanism(
+      lambda: rdp_privacy_accountant.RdpAccountant(orders),
       make_event_from_param,
       epsilon,
       delta,
-      dp_accounting.LowerEndpointAndGuess(0, 1),
+      mechanism_calibration.LowerEndpointAndGuess(0, 1),
       tol=tolerance)
 
 
diff --git a/tutorials/BUILD b/tutorials/BUILD
index 0a8f487..983f869 100644
--- a/tutorials/BUILD
+++ b/tutorials/BUILD
@@ -27,7 +27,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
@@ -38,7 +39,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer_keras",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
@@ -49,7 +51,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/keras_models:dp_keras_model",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
@@ -60,7 +63,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer_vectorized",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
@@ -83,7 +87,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
@@ -94,7 +99,8 @@ py_binary(
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
-        "@com_google_differential_py//python/dp_accounting",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
diff --git a/tutorials/lm_dpsgd_tutorial.py b/tutorials/lm_dpsgd_tutorial.py
index ed01ca5..10448f0 100644
--- a/tutorials/lm_dpsgd_tutorial.py
+++ b/tutorials/lm_dpsgd_tutorial.py
@@ -35,13 +35,15 @@ import os
 from absl import app
 from absl import flags
 from absl import logging
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
 from tensorflow import estimator as tf_estimator
 from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
 import tensorflow_datasets as tfds
+
 from tensorflow_privacy.privacy.optimizers import dp_optimizer
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 
 flags.DEFINE_boolean(
@@ -151,11 +153,11 @@ def compute_epsilon(steps):
   orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
   sampling_probability = FLAGS.batch_size / NB_TRAIN
 
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
           sampling_probability,
-          dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
+          dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
   accountant.compose(event)
 
   # Delta is set to 1e-5 because Penn TreeBank has 60000 training points.
diff --git a/tutorials/mnist_dpsgd_tutorial_eager.py b/tutorials/mnist_dpsgd_tutorial_eager.py
index 0e105a6..9d306d7 100644
--- a/tutorials/mnist_dpsgd_tutorial_eager.py
+++ b/tutorials/mnist_dpsgd_tutorial_eager.py
@@ -15,11 +15,14 @@
 
 from absl import app
 from absl import flags
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
 from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
 
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
+
+
 GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
 tf.compat.v1.enable_eager_execution()
 
@@ -44,13 +47,13 @@ def compute_epsilon(steps):
   if FLAGS.noise_multiplier == 0.0:
     return float('inf')
   orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
 
   sampling_probability = FLAGS.batch_size / 60000
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
           sampling_probability,
-          dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
+          dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
 
   accountant.compose(event)
 
diff --git a/tutorials/mnist_dpsgd_tutorial_keras.py b/tutorials/mnist_dpsgd_tutorial_keras.py
index 5265ee4..2d57bcd 100644
--- a/tutorials/mnist_dpsgd_tutorial_keras.py
+++ b/tutorials/mnist_dpsgd_tutorial_keras.py
@@ -16,11 +16,14 @@
 from absl import app
 from absl import flags
 from absl import logging
-from com_google_differential_py.python.dp_accounting
+
 import numpy as np
 import tensorflow as tf
+
 from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
 
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 flags.DEFINE_boolean(
     'dpsgd', True, 'If True, train with DP-SGD. If False, '
@@ -44,13 +47,13 @@ def compute_epsilon(steps):
   if FLAGS.noise_multiplier == 0.0:
     return float('inf')
   orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
 
   sampling_probability = FLAGS.batch_size / 60000
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
           sampling_probability,
-          dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
+          dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
 
   accountant.compose(event)
 
diff --git a/tutorials/mnist_dpsgd_tutorial_keras_model.py b/tutorials/mnist_dpsgd_tutorial_keras_model.py
index 497e7a2..0212f6b 100644
--- a/tutorials/mnist_dpsgd_tutorial_keras_model.py
+++ b/tutorials/mnist_dpsgd_tutorial_keras_model.py
@@ -16,11 +16,13 @@
 from absl import app
 from absl import flags
 from absl import logging
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
+
 from tensorflow_privacy.privacy.keras_models.dp_keras_model import DPSequential
 
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 flags.DEFINE_boolean(
     'dpsgd', True, 'If True, train with DP-SGD. If False, '
@@ -44,13 +46,13 @@ def compute_epsilon(steps):
   if FLAGS.noise_multiplier == 0.0:
     return float('inf')
   orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
 
   sampling_probability = FLAGS.batch_size / 60000
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
           sampling_probability,
-          dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
+          dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
 
   accountant.compose(event)
 
diff --git a/tutorials/mnist_dpsgd_tutorial_vectorized.py b/tutorials/mnist_dpsgd_tutorial_vectorized.py
index 9accb4f..142d279 100644
--- a/tutorials/mnist_dpsgd_tutorial_vectorized.py
+++ b/tutorials/mnist_dpsgd_tutorial_vectorized.py
@@ -16,13 +16,14 @@
 from absl import app
 from absl import flags
 from absl import logging
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
 from tensorflow import estimator as tf_estimator
 from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
 from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
 
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 flags.DEFINE_boolean(
     'dpsgd', True, 'If True, train with DP-SGD. If False, '
@@ -50,13 +51,13 @@ def compute_epsilon(steps):
   if FLAGS.noise_multiplier == 0.0:
     return float('inf')
   orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
 
   sampling_probability = FLAGS.batch_size / 60000
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
           sampling_probability,
-          dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
+          dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
 
   accountant.compose(event)
 
diff --git a/tutorials/mnist_lr_tutorial.py b/tutorials/mnist_lr_tutorial.py
index ee8e346..0da8032 100644
--- a/tutorials/mnist_lr_tutorial.py
+++ b/tutorials/mnist_lr_tutorial.py
@@ -26,12 +26,13 @@ import math
 from absl import app
 from absl import flags
 from absl import logging
-from com_google_differential_py.python.dp_accounting
 import numpy as np
 import tensorflow as tf
 from tensorflow import estimator as tf_estimator
 from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
 from tensorflow_privacy.privacy.optimizers import dp_optimizer
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
 
@@ -165,14 +166,13 @@ def print_privacy_guarantees(epochs, batch_size, samples, noise_multiplier):
     # Using RDP accountant to compute eps. Doing computation analytically is
     # an option.
     rdp = [order * coef for order in orders]
-    eps = dp_accounting.rdp.compute_epsilon(orders, rdp, delta)
+    eps = rdp_privacy_accountant.compute_epsilon(orders, rdp, delta)
     print('\t{:g}% enjoy at least ({:.2f}, {})-DP'.format(p * 100, eps, delta))
 
-  accountant = dp_accounting.rdp.RdpAccountant(orders)
-  event = dp_accounting.SelfComposedDpEvent(
-      dp_accounting.PoissonSampledDpEvent(
-          batch_size / samples,
-          dp_accounting.GaussianDpEvent(noise_multiplier)),
+  accountant = rdp_privacy_accountant.RdpAccountant(orders)
+  event = dp_event.SelfComposedDpEvent(
+      dp_event.PoissonSampledDpEvent(
+          batch_size / samples, dp_event.GaussianDpEvent(noise_multiplier)),
       epochs * steps_per_epoch)
   accountant.compose(event)
   eps_sgm = accountant.get_epsilon(target_delta=delta)