perform_cross_partition

pipeline_dp/aggregate_params.py

@@ -234,6 +234,11 @@ class AggregateParams:
          is ignored when public partitions are used.
          More details on pre-thresholding are in
          https://github.com/google/differential-privacy/blob/main/common_docs/pre_thresholding.md
+        perform_cross_partition_contribution_bounding: whether to perform cross
+         partition contribution bounding.  
+         Warning: turn off cross partition contribution bounding only when the 
+         number of contributed partitions per privacy unit is already bounded
+         by max_partitions_contributed.
     """
     metrics: List[Metric]
     noise_kind: NoiseKind = NoiseKind.LAPLACE

pipeline_dp/contribution_bounders.py

@@ -22,6 +22,7 @@
 from pipeline_dp import sampling_utils
 
 
+# TODO: rename ContributionBounder -> Sampler
 class ContributionBounder(abc.ABC):
     """Interface for objects which perform contribution bounding."""
 
@@ -76,8 +77,8 @@ def bound_contributions(self, col, params, backend, report_generator,
             "Sample per (privacy_id, partition_key)")
         report_generator.add_stage(
             f"Per-partition contribution bounding: for each privacy_id and each"
-            f"partition, randomly select max(actual_contributions_per_partition"
-            f", {max_contributions_per_partition}) contributions.")
+            f" partition, randomly select max(actual_contributions_per_partitio"
+            f"n, {max_contributions_per_partition}) contributions.")
         # ((privacy_id, partition_key), [value])
         col = backend.map_values(
             col, aggregate_fn,
@@ -195,7 +196,7 @@ def rekey_per_privacy_id_per_partition_key(pid_pk_v_values):
             "Apply aggregate_fn after cross-partition contribution bounding")
 
 
-class SamplingPerPartitionContributionBounder(ContributionBounder):
+class LinfSampler(ContributionBounder):
     """Bounds the contribution of privacy_id cross partitions.
 
     It ensures that each privacy_id contributes to not more than
@@ -215,19 +216,18 @@ def bound_contributions(self, col, params, backend, report_generator,
             "Sample per (privacy_id, partition_key)")
         # ((privacy_id, partition_key), value)
 
+        report_generator.add_stage(
+            f"Per-partition contribution bounding: for each privacy_id and each"
+            f" partition, randomly select max(actual_contributions_per_partitio"
+            f"n, {params.max_contributions_per_partition}) contributions.")
+
         return backend.map_values(
             col, aggregate_fn,
             "Apply aggregate_fn after cross-partition contribution bounding")
 
 
 class NoOpContributionBounder(ContributionBounder):
-    """Bounds the contribution of privacy_id cross partitions.
-
-    It ensures that each privacy_id contributes to not more than
-    max_partitions_contributed partitions (cross-partition contribution
-    bounding), by performing sampling if needed. It is assumed that the provided
-    aggregate_fn function bounds per-partition contributions.
-    """
+    """Does no sampling."""
 
     def bound_contributions(self, col, params, backend, report_generator,
                             aggregate_fn):
@@ -236,9 +236,10 @@ def bound_contributions(self, col, params, backend, report_generator,
             "Rekey to ((privacy_id, partition_key), value)")
         # ((privacy_id, partition_key), value)
 
-        return backend.group_by_key(
-            col, aggregate_fn,
-            "Apply aggregate_fn after cross-partition contribution bounding")
+        col = backend.group_by_key(col, "Group by (privacy_id, partition_key)")
+        # ((privacy_id, partition_key), [value])
+
+        return backend.map_values(col, aggregate_fn, "Apply aggregate_fn")
 
 
 def collect_values_per_partition_key_per_privacy_id(

pipeline_dp/dp_engine.py

@@ -386,10 +386,18 @@ def _create_contribution_bounder(
             return \
                 contribution_bounders.SamplingPerPrivacyIdContributionBounder(
                 )
-        if expects_per_partition_sampling:
-            return contribution_bounders.SamplingCrossAndPerPartitionContributionBounder(
+        if params.perform_cross_partition_contribution_bounding:
+            if expects_per_partition_sampling:
+                return contribution_bounders.SamplingCrossAndPerPartitionContributionBounder(
+                )
+            return contribution_bounders.SamplingCrossPartitionContributionBounder(
             )
-        return contribution_bounders.SamplingCrossPartitionContributionBounder()
+        # no cross partition contribution
+        if expects_per_partition_sampling:
+            return contribution_bounders.LinfSampler()
+        # No sampling, but combiners themselves do per partition contribution
+        # bounding.
+        return contribution_bounders.NoOpContributionBounder()
 
     def _extract_columns(self, col,
                          data_extractors: pipeline_dp.DataExtractors):