More detailed description of arguments in compute_dp_sgd_privacy.

PiperOrigin-RevId: 522693217

Author: galenmandrew

tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy.py

@@ -34,10 +34,24 @@
 from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy_statement
 
 
-_NUM_EXAMPLES = flags.DEFINE_integer('N', None, 'Total number of examples.')
-_BATCH_SIZE = flags.DEFINE_integer('batch_size', None, 'Batch size.')
+_NUM_EXAMPLES = flags.DEFINE_integer(
+    'N', None, 'Total number of examples in the training data.'
+)
+_BATCH_SIZE = flags.DEFINE_integer(
+    'batch_size',
+    None,
+    (
+        'Number of examples in a batch *regardless of how/whether they are '
+        'grouped into microbatches*.'
+    ),
+)
 _NOISE_MULTIPLIER = flags.DEFINE_float(
-    'noise_multiplier', None, 'Noise multiplier for DP-SGD.'
+    'noise_multiplier',
+    None,
+    (
+        'Noise multiplier for DP-SGD: ratio of Gaussian noise stddev to the '
+        'l2 clip norm at each round.'
+    ),
 )
 _NUM_EPOCHS = flags.DEFINE_float(
     'epochs', None, 'Number of epochs (may be fractional).'
@@ -52,8 +66,8 @@
     'max_examples_per_user',
     None,
     (
-        'Maximum number of examples per user, applicable. Used to compute a'
-        ' user-level DP guarantee.'
+        'Maximum number of examples per user, if applicable. Used to compute a '
+        'user-level DP guarantee.'
     ),
 )
 

tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py

@@ -51,7 +51,7 @@ def _compute_dp_sgd_user_privacy(
 
   Args:
     num_epochs: The number of passes over the data. May be fractional.
-    noise_multiplier: The ratio of the noise to the l2 sensitivity.
+    noise_multiplier: The ratio of the noise stddev to the l2 sensitivity.
     user_delta: The target user-level delta.
     max_examples_per_user: Upper bound on the number of examples per user.
     used_microbatching: If true, increases sensitivity by a factor of two.
@@ -183,7 +183,7 @@ def _compute_dp_sgd_example_privacy(
 
   Args:
     num_epochs: The number of passes over the data.
-    noise_multiplier: The ratio of the noise to the l2 sensitivity.
+    noise_multiplier: The ratio of the noise stddev to the l2 sensitivity.
     example_delta: The target delta.
     used_microbatching: If true, increases sensitivity by a factor of two.
     poisson_subsampling_probability: If not None, gives the probability that
@@ -244,9 +244,10 @@ def compute_dp_sgd_privacy_statement(
       examples in a batch, *regardless of whether/how they are grouped into
       microbatches*.
     num_epochs: The number of epochs of training. May be fractional.
-    noise_multiplier: The ratio of the Gaussian noise to the clip norm at each
-      round. It is assumed that the noise_multiplier is constant although the
-      clip norm may be variable if, for example, adaptive clipping is used.
+    noise_multiplier: The ratio of the Gaussian noise stddev to the l2 clip norm
+      at each round. It is assumed that the noise_multiplier is constant
+      although the clip norm may be variable if, for example, adaptive clipping
+      is used.
     delta: The target delta.
     used_microbatching: Whether microbatching was used (with microbatch size
       greater than one). Microbatching inflates sensitivity by a factor of two