diff --git a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py index 5717e4f..990391b 100644 --- a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py +++ b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py @@ -15,13 +15,10 @@ `TreeCumulativeSumQuery` and `TreeResidualSumQuery` are `DPQuery`s for continual online observation queries relying on `tree_aggregation`. 'Online' means that -the leaf nodes of the tree arrive one by one as the time proceeds. The leaves -are vector records as defined in `dp_query.DPQuery`. +the leaf nodes of the tree arrive one by one as the time proceeds. -`CentralTreeSumQuery` and `DistributedTreeSumQuery` are `DPQuery`s for -central/distributed offline tree aggregation protocol. 'Offline' means all the -leaf nodes are ready before the protocol starts. Each record, different from -what is defined in `dp_query.DPQuery`, is a histogram (i.e. the leaf nodes). +`TreeRangeSumQuery` is a `DPQuery`s for offline tree aggregation protocol. +'Offline' means all the leaf nodes are ready before the protocol starts. """ import distutils import math @@ -29,7 +26,9 @@ from typing import Optional import attr 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 tensorflow_privacy.privacy.dp_query import tree_aggregation @@ -442,6 +441,171 @@ def _build_tree_from_leaf(leaf_nodes: tf.Tensor, arity: int) -> tf.RaggedTensor: return tree +class TreeRangeSumQuery(dp_query.SumAggregationDPQuery): + """Implements dp_query for accurate range queries using tree aggregation. + + Implements a variant of the tree aggregation protocol from. "Is interaction + necessary for distributed private learning?. Adam Smith, Abhradeep Thakurta, + Jalaj Upadhyay." Builds a tree on top of the input record and adds noise to + the tree for differential privacy. Any range query can be decomposed into the + sum of O(log(n)) nodes in the tree compared to O(n) when using a histogram. + Improves efficiency and reduces noise scale. + """ + + @attr.s(frozen=True) + class GlobalState(object): + """Class defining global state for TreeRangeSumQuery. + + Attributes: + arity: The branching factor of the tree (i.e. the number of children each + internal node has). + inner_query_state: The global state of the inner query. + """ + arity = attr.ib() + inner_query_state = attr.ib() + + def __init__(self, + inner_query: dp_query.SumAggregationDPQuery, + arity: int = 2): + """Initializes the `TreeRangeSumQuery`. + + Args: + inner_query: The inner `DPQuery` that adds noise to the tree. + arity: The branching factor of the tree (i.e. the number of children each + internal node has). Defaults to 2. + """ + self._inner_query = inner_query + self._arity = arity + + if self._arity < 1: + raise ValueError(f'Invalid arity={arity} smaller than 2.') + + def initial_global_state(self): + """Implements `tensorflow_privacy.DPQuery.initial_global_state`.""" + return TreeRangeSumQuery.GlobalState( + arity=self._arity, + inner_query_state=self._inner_query.initial_global_state()) + + def derive_sample_params(self, global_state): + """Implements `tensorflow_privacy.DPQuery.derive_sample_params`.""" + return (global_state.arity, + self._inner_query.derive_sample_params( + global_state.inner_query_state)) + + def preprocess_record(self, params, record): + """Implements `tensorflow_privacy.DPQuery.preprocess_record`. + + This method builds the tree, flattens it and applies + `inner_query.preprocess_record` to the flattened tree. + + Args: + params: Hyper-parameters for preprocessing record. + record: A histogram representing the leaf nodes of the tree. + + Returns: + A `tf.Tensor` representing the flattened version of the preprocessed tree. + """ + arity, inner_query_params = params + preprocessed_record = _build_tree_from_leaf(record, arity).flat_values + preprocessed_record = self._inner_query.preprocess_record( + inner_query_params, preprocessed_record) + + # The following codes reshape the output vector so the output shape of can + # be statically inferred. This is useful when used with + # `tff.aggregators.DifferentiallyPrivateFactory` because it needs to know + # the output shape of this function statically and explicitly. + preprocessed_record_shape = [ + (self._arity**(math.ceil(math.log(record.shape[0], self._arity)) + 1) - + 1) // (self._arity - 1) + ] + return tf.reshape(preprocessed_record, preprocessed_record_shape) + + def get_noised_result(self, sample_state, global_state): + """Implements `tensorflow_privacy.DPQuery.get_noised_result`. + + This function re-constructs the `tf.RaggedTensor` from the flattened tree + output by `preprocess_records.` + + Args: + sample_state: A `tf.Tensor` for the flattened tree. + global_state: The global state of the protocol. + + Returns: + A `tf.RaggedTensor` representing the tree. + """ + # The [0] is needed because of how tf.RaggedTensor.from_two_splits works. + # print(tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6], + # row_splits=[0, 4, 4, 7, 8, 8])) + # + # This part is not written in tensorflow and will be executed on the server + # side instead of the client side if used with + # tff.aggregators.DifferentiallyPrivateFactory for federated learning. + row_splits = [0] + [ + (self._arity**(x + 1) - 1) // (self._arity - 1) for x in range( + math.floor(math.log(sample_state.shape[0], self._arity)) + 1) + ] + tree = tf.RaggedTensor.from_row_splits( + values=sample_state, row_splits=row_splits) + return tree, global_state + + @classmethod + def build_central_gaussian_query(cls, + l2_norm_clip: float, + stddev: float, + arity: int = 2): + """Returns `TreeRangeSumQuery` with central Gaussian noise. + + Args: + l2_norm_clip: Each record should be clipped so that it has L2 norm at most + `l2_norm_clip`. + stddev: Stddev of the central Gaussian noise. + arity: The branching factor of the tree (i.e. the number of children each + internal node has). Defaults to 2. + """ + if l2_norm_clip <= 0: + raise ValueError(f'`l2_norm_clip` must be positive, got {l2_norm_clip}.') + + if stddev < 0: + raise ValueError(f'`stddev` must be non-negative, got {stddev}.') + + if arity < 2: + raise ValueError(f'`arity` must be at least 2, got {arity}.') + + inner_query = gaussian_query.GaussianSumQuery(l2_norm_clip, stddev) + + return cls(arity=arity, inner_query=inner_query) + + @classmethod + def build_distributed_discrete_gaussian_query(cls, + l2_norm_bound: float, + local_stddev: float, + arity: int = 2): + """Returns `TreeRangeSumQuery` with central Gaussian noise. + + Args: + l2_norm_bound: Each record should be clipped so that it has L2 norm at + most `l2_norm_bound`. + local_stddev: Scale/stddev of the local discrete Gaussian noise. + arity: The branching factor of the tree (i.e. the number of children each + internal node has). Defaults to 2. + """ + if l2_norm_bound <= 0: + raise ValueError( + f'`l2_clip_bound` must be positive, got {l2_norm_bound}.') + + if local_stddev < 0: + raise ValueError( + f'`local_stddev` must be non-negative, got {local_stddev}.') + + if arity < 2: + raise ValueError(f'`arity` must be at least 2, got {arity}.') + + inner_query = distributed_discrete_gaussian_query.DistributedDiscreteGaussianSumQuery( + l2_norm_bound, local_stddev) + + return cls(arity=arity, inner_query=inner_query) + + def _get_add_noise(stddev, seed: int = None): """Utility function to decide which `add_noise` to use according to tf version.""" if distutils.version.LooseVersion( diff --git a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query_test.py b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query_test.py index cc3a89a..a958f26 100644 --- a/tensorflow_privacy/privacy/dp_query/tree_aggregation_query_test.py +++ b/tensorflow_privacy/privacy/dp_query/tree_aggregation_query_test.py @@ -423,6 +423,137 @@ class BuildTreeTest(tf.test.TestCase, parameterized.TestCase): self.assertEqual(tree[layer][idx], expected_value) +class TreeRangeSumQueryTest(tf.test.TestCase, parameterized.TestCase): + + @parameterized.product( + inner_query=['central', 'distributed'], + params=[(0., 1., 2), (1., -1., 2), (1., 1., 1)], + ) + def test_raises_error(self, inner_query, params): + clip_norm, stddev, arity = params + with self.assertRaises(ValueError): + if inner_query == 'central': + tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + clip_norm, stddev, arity) + elif inner_query == 'distributed': + tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + clip_norm, stddev, arity) + + @parameterized.product( + inner_query=['central', 'distributed'], + clip_norm=[0.1, 1.0, 10.0], + stddev=[0.1, 1.0, 10.0]) + def test_initial_global_state_type(self, inner_query, clip_norm, stddev): + + if inner_query == 'central': + query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + clip_norm, stddev) + elif inner_query == 'distributed': + query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + clip_norm, stddev) + global_state = query.initial_global_state() + self.assertIsInstance(global_state, + tree_aggregation_query.TreeRangeSumQuery.GlobalState) + + @parameterized.product( + inner_query=['central', 'distributed'], + clip_norm=[0.1, 1.0, 10.0], + stddev=[0.1, 1.0, 10.0], + arity=[2, 3, 4]) + def test_derive_sample_params(self, inner_query, clip_norm, stddev, arity): + if inner_query == 'central': + query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + clip_norm, stddev, arity) + elif inner_query == 'distributed': + query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + clip_norm, stddev, arity) + global_state = query.initial_global_state() + derived_arity, inner_query_state = query.derive_sample_params(global_state) + self.assertAllClose(derived_arity, arity) + if inner_query == 'central': + self.assertAllClose(inner_query_state, clip_norm) + elif inner_query == 'distributed': + self.assertAllClose(inner_query_state.l2_norm_bound, clip_norm) + self.assertAllClose(inner_query_state.local_stddev, stddev) + + @parameterized.product( + (dict(arity=2, expected_tree=[1, 1, 0, 1, 0, 0, 0]), + dict(arity=3, expected_tree=[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])), + inner_query=['central', 'distributed'], + ) + def test_preprocess_record(self, inner_query, arity, expected_tree): + if inner_query == 'central': + query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + 10., 0., arity) + record = tf.constant([1, 0, 0, 0], dtype=tf.float32) + expected_tree = tf.cast(expected_tree, tf.float32) + elif inner_query == 'distributed': + query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + 10., 0., arity) + record = tf.constant([1, 0, 0, 0], dtype=tf.int32) + global_state = query.initial_global_state() + params = query.derive_sample_params(global_state) + preprocessed_record = query.preprocess_record(params, record) + self.assertAllClose(preprocessed_record, expected_tree) + + @parameterized.named_parameters( + ('stddev_1', 1, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]), + ('stddev_0_1', 4, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]), + ) + def test_distributed_preprocess_record_with_noise(self, local_stddev, record, + expected_tree): + query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + 10., local_stddev) + global_state = query.initial_global_state() + params = query.derive_sample_params(global_state) + + preprocessed_record = query.preprocess_record(params, record) + + self.assertAllClose( + preprocessed_record, expected_tree, atol=10 * local_stddev) + + @parameterized.product( + (dict( + arity=2, + expected_tree=tf.ragged.constant([[1], [1, 0], [1, 0, 0, 0]])), + dict( + arity=3, + expected_tree=tf.ragged.constant([[1], [1, 0, 0], + [1, 0, 0, 0, 0, 0, 0, 0, 0]]))), + inner_query=['central', 'distributed'], + ) + def test_get_noised_result(self, inner_query, arity, expected_tree): + if inner_query == 'central': + query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + 10., 0., arity) + record = tf.constant([1, 0, 0, 0], dtype=tf.float32) + expected_tree = tf.cast(expected_tree, tf.float32) + elif inner_query == 'distributed': + query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query( + 10., 0., arity) + record = tf.constant([1, 0, 0, 0], dtype=tf.int32) + global_state = query.initial_global_state() + params = query.derive_sample_params(global_state) + preprocessed_record = query.preprocess_record(params, record) + sample_state, global_state = query.get_noised_result( + preprocessed_record, global_state) + + self.assertAllClose(sample_state, expected_tree) + + @parameterized.product(stddev=[0.1, 1.0, 10.0]) + def test_central_get_noised_result_with_noise(self, stddev): + query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query( + 10., stddev) + global_state = query.initial_global_state() + params = query.derive_sample_params(global_state) + preprocessed_record = query.preprocess_record(params, tf.constant([1., 0.])) + sample_state, global_state = query.get_noised_result( + preprocessed_record, global_state) + + self.assertAllClose( + sample_state, tf.ragged.constant([[1.], [1., 0.]]), atol=10 * stddev) + + class CentralTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase): def test_initial_global_state_type(self):