Wennan Zhu
A. Unique TensorFlower
parent
ef83391ce6
commit
b9e4cf1a20
2 changed files with 0 additions and 459 deletions
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@ -22,7 +22,6 @@ the leaf nodes of the tree arrive one by one as the time proceeds.
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"""
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import distutils
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import math
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from typing import Optional
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import attr
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import tensorflow as tf
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@ -732,224 +731,3 @@ def _get_add_noise(stddev, seed: int = None):
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return v + tf.cast(random_normal(tf.shape(input=v)), dtype=v.dtype)
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return add_noise
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class CentralTreeSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements dp_query for differentially private tree aggregation protocol.
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Implements a central variant of the tree aggregation protocol from the paper
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"'Is interaction necessary for distributed private learning?.' Adam Smith,
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Abhradeep Thakurta, Jalaj Upadhyay" by replacing their local randomizer with
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gaussian mechanism. The first step is to clip the clients' local updates (i.e.
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a 1-D array containing the leaf nodes of the tree) by L1 norm to make sure it
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does not exceed a prespecified upper bound. The second step is to construct
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the tree on the clipped update. The third step is to add independent gaussian
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noise to each node in the tree. The returned tree can support efficient and
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accurate range queries with differential privacy.
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"""
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@attr.s(frozen=True)
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class GlobalState(object):
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"""Class defining global state for `CentralTreeSumQuery`.
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Attributes:
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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"""
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l1_bound = attr.ib()
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def __init__(self,
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stddev: float,
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arity: int = 2,
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l1_bound: int = 10,
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seed: Optional[int] = None):
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"""Initializes the `CentralTreeSumQuery`.
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Args:
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stddev: The stddev of the noise added to each internal node of the
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constructed tree.
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arity: The branching factor of the tree.
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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seed: Random seed to generate Gaussian noise. Defaults to `None`. Only for
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test purpose.
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"""
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self._stddev = stddev
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self._arity = arity
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self._l1_bound = l1_bound
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self._seed = seed
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def initial_global_state(self):
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"""Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
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return CentralTreeSumQuery.GlobalState(l1_bound=self._l1_bound)
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def derive_sample_params(self, global_state):
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"""Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
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return global_state.l1_bound
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def preprocess_record(self, params, record):
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"""Implements `tensorflow_privacy.DPQuery.preprocess_record`."""
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casted_record = tf.cast(record, tf.float32)
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l1_norm = tf.norm(casted_record, ord=1)
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l1_bound = tf.cast(params, tf.float32)
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preprocessed_record, _ = tf.clip_by_global_norm([casted_record],
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l1_bound,
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use_norm=l1_norm)
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return preprocessed_record[0]
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def get_noised_result(self, sample_state, global_state):
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"""Implements `tensorflow_privacy.DPQuery.get_noised_result`.
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Args:
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sample_state: a frequency histogram.
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global_state: hyper-parameters of the query.
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Returns:
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a `tf.RaggedTensor` representing the tree built on top of `sample_state`.
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The jth node on the ith layer of the tree can be accessed by tree[i][j]
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where tree is the returned value.
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"""
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add_noise = _get_add_noise(self._stddev, self._seed)
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tree = _build_tree_from_leaf(sample_state, self._arity)
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return tf.map_fn(add_noise, tree), global_state
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class DistributedTreeSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements dp_query for differentially private tree aggregation protocol.
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The difference from `CentralTreeSumQuery` is that the tree construction and
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gaussian noise addition happen in `preprocess_records`. The difference only
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takes effect when used together with
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`tff.aggregators.DifferentiallyPrivateFactory`. In other cases, this class
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should be treated as equal with `CentralTreeSumQuery`.
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Implements a distributed version of the tree aggregation protocol from. "Is
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interaction necessary for distributed private learning?." by replacing their
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local randomizer with gaussian mechanism. The first step is to check the L1
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norm of the clients' local updates (i.e. a 1-D array containing the leaf nodes
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of the tree) to make sure it does not exceed a prespecified upper bound. The
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second step is to construct the tree. The third step is to add independent
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gaussian noise to each node in the tree. The returned tree can support
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efficient and accurate range queries with differential privacy.
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"""
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@attr.s(frozen=True)
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class GlobalState(object):
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"""Class defining global state for DistributedTreeSumQuery.
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Attributes:
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stddev: The stddev of the noise added to each internal node in the
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constructed tree.
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arity: The branching factor of the tree (i.e. the number of children each
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internal node has).
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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"""
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stddev = attr.ib()
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arity = attr.ib()
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l1_bound = attr.ib()
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def __init__(self,
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stddev: float,
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arity: int = 2,
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l1_bound: int = 10,
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seed: Optional[int] = None):
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"""Initializes the `DistributedTreeSumQuery`.
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Args:
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stddev: The stddev of the noise added to each node in the tree.
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arity: The branching factor of the tree.
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l1_bound: An upper bound on the L1 norm of the input record. This is
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needed to bound the sensitivity and deploy differential privacy.
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seed: Random seed to generate Gaussian noise. Defaults to `None`. Only for
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test purpose.
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"""
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self._stddev = stddev
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self._arity = arity
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self._l1_bound = l1_bound
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self._seed = seed
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def initial_global_state(self):
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"""Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
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return DistributedTreeSumQuery.GlobalState(
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stddev=self._stddev, arity=self._arity, l1_bound=self._l1_bound)
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def derive_sample_params(self, global_state):
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"""Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
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return (global_state.stddev, global_state.arity, global_state.l1_bound)
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def preprocess_record(self, params, record):
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"""Implements `tensorflow_privacy.DPQuery.preprocess_record`.
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This method clips the input record by L1 norm, constructs a tree on top of
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it, and adds gaussian noise to each node of the tree for differential
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privacy. Unlike `get_noised_result` in `CentralTreeSumQuery`, this function
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flattens the `tf.RaggedTensor` before outputting it. This is useful when
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used inside `tff.aggregators.DifferentiallyPrivateFactory` because it does
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not accept ragged output tensor.
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Args:
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params: hyper-parameters for preprocessing record, (stddev, aritry,
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l1_bound)
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record: leaf nodes for the tree.
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Returns:
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`tf.Tensor` representing the flattened version of the tree.
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"""
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_, arity, l1_bound_ = params
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l1_bound = tf.cast(l1_bound_, tf.float32)
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casted_record = tf.cast(record, tf.float32)
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l1_norm = tf.norm(casted_record, ord=1)
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preprocessed_record, _ = tf.clip_by_global_norm([casted_record],
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l1_bound,
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use_norm=l1_norm)
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preprocessed_record = preprocessed_record[0]
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add_noise = _get_add_noise(self._stddev, self._seed)
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tree = _build_tree_from_leaf(preprocessed_record, arity)
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noisy_tree = tf.map_fn(add_noise, tree)
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# The following codes reshape the output vector so the output shape of can
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# be statically inferred. This is useful when used with
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# `tff.aggregators.DifferentiallyPrivateFactory` because it needs to know
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# the output shape of this function statically and explicitly.
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flat_noisy_tree = noisy_tree.flat_values
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flat_tree_shape = [
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(self._arity**(math.ceil(math.log(record.shape[0], self._arity)) + 1) -
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1) // (self._arity - 1)
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]
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return tf.reshape(flat_noisy_tree, flat_tree_shape)
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def get_noised_result(self, sample_state, global_state):
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"""Implements `tensorflow_privacy.DPQuery.get_noised_result`.
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This function re-constructs the `tf.RaggedTensor` from the flattened tree
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output by `preprocess_records.`
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Args:
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sample_state: `tf.Tensor` for the flattened tree.
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global_state: hyper-parameters including noise multiplier, the branching
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factor of the tree and the maximum records per user.
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Returns:
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a `tf.RaggedTensor` for the tree.
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"""
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# The [0] is needed because of how tf.RaggedTensor.from_two_splits works.
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# print(tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6],
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# row_splits=[0, 4, 4, 7, 8, 8]))
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# <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
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# This part is not written in tensorflow and will be executed on the server
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# side instead of the client side if used with
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# tff.aggregators.DifferentiallyPrivateFactory for federated learning.
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row_splits = [0] + [
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(self._arity**(x + 1) - 1) // (self._arity - 1) for x in range(
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math.floor(math.log(sample_state.shape[0], self._arity)) + 1)
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]
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tree = tf.RaggedTensor.from_row_splits(
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values=sample_state, row_splits=row_splits)
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return tree, global_state
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@ -630,242 +630,5 @@ class TreeRangeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
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sample_state, tf.ragged.constant([[1.], [1., 0.]]), atol=10 * stddev)
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class CentralTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
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def test_initial_global_state_type(self):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=NOISE_STD)
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global_state = query.initial_global_state()
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self.assertIsInstance(
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global_state, tree_aggregation_query.CentralTreeSumQuery.GlobalState)
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def test_derive_sample_params(self):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=NOISE_STD)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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self.assertAllClose(params, 10.)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32)),
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('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32)),
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('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32)),
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('ternary_test_float', 3, tf.constant([1., 0., 0., 0.],
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dtype=tf.float32)),
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)
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def test_preprocess_record(self, arity, record):
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query = tree_aggregation_query.CentralTreeSumQuery(
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stddev=NOISE_STD, arity=arity)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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self.assertAllClose(preprocessed_record, record)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
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tf.constant([5, 5, 0, 0], dtype=tf.int32)),
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('binary_test_float', 2, tf.constant(
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[10., 10., 0., 0.],
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dtype=tf.float32), tf.constant([5., 5., 0., 0.], dtype=tf.float32)),
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('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
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tf.constant([5, 5, 0, 0], dtype=tf.int32)),
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('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
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dtype=tf.float32),
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tf.constant([5., 5., 0., 0.], dtype=tf.float32)),
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)
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def test_preprocess_record_clipped(self, arity, record,
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expected_clipped_value):
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query = tree_aggregation_query.CentralTreeSumQuery(
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stddev=NOISE_STD, arity=arity)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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self.assertAllClose(preprocessed_record, expected_clipped_value)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
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tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
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('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
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tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
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('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
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tf.ragged.constant([[1.], [1., 0., 0.],
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[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
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('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
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tf.ragged.constant([[1.], [1., 0., 0.],
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[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
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)
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def test_get_noised_result(self, arity, record, expected_tree):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=0., arity=arity)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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sample_state, global_state = query.get_noised_result(
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preprocessed_record, global_state)
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self.assertAllClose(sample_state, expected_tree)
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@parameterized.named_parameters(
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('stddev_0_01', 0.01, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
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('stddev_0_1', 0.1, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
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)
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def test_get_noised_result_with_noise(self, stddev, record, expected_tree):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=stddev, seed=0)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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sample_state, _ = query.get_noised_result(preprocessed_record, global_state)
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self.assertAllClose(
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sample_state.flat_values, expected_tree, atol=3 * stddev)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
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tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
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('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
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dtype=tf.float32),
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tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
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('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
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tf.ragged.constant([[10.], [10., 0., 0.],
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[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
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('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
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dtype=tf.float32),
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tf.ragged.constant([[10.], [10., 0., 0.],
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[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
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)
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def test_get_noised_result_clipped(self, arity, record, expected_tree):
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query = tree_aggregation_query.CentralTreeSumQuery(stddev=0., arity=arity)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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sample_state, global_state = query.get_noised_result(
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preprocessed_record, global_state)
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self.assertAllClose(sample_state, expected_tree)
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class DistributedTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
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def test_initial_global_state_type(self):
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query = tree_aggregation_query.DistributedTreeSumQuery(stddev=NOISE_STD)
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global_state = query.initial_global_state()
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self.assertIsInstance(
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global_state,
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tree_aggregation_query.DistributedTreeSumQuery.GlobalState)
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def test_derive_sample_params(self):
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query = tree_aggregation_query.DistributedTreeSumQuery(stddev=NOISE_STD)
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global_state = query.initial_global_state()
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stddev, arity, l1_bound = query.derive_sample_params(global_state)
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self.assertAllClose(stddev, NOISE_STD)
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self.assertAllClose(arity, 2)
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self.assertAllClose(l1_bound, 10)
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@parameterized.named_parameters(
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('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
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tf.ragged.constant([1., 1., 0., 1., 0., 0., 0.])),
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('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
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tf.ragged.constant([1., 1., 0., 1., 0., 0., 0.])),
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('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
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tf.ragged.constant([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.
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])),
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('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
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tf.ragged.constant([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.
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])),
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)
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def test_preprocess_record(self, arity, record, expected_tree):
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query = tree_aggregation_query.DistributedTreeSumQuery(
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stddev=0., arity=arity)
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global_state = query.initial_global_state()
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params = query.derive_sample_params(global_state)
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preprocessed_record = query.preprocess_record(params, record)
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self.assertAllClose(preprocessed_record, expected_tree)
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@parameterized.named_parameters(
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('stddev_0_01', 0.01, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
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('stddev_0_1', 0.1, tf.constant([1, 0], dtype=tf.int32), [1., 1., 0.]),
|
||||
)
|
||||
def test_preprocess_record_with_noise(self, stddev, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=stddev, seed=0)
|
||||
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=3 * stddev)
|
||||
|
||||
@parameterized.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([10., 10., 0., 5., 5., 0., 0.])),
|
||||
('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([10., 10., 0., 5., 5., 0., 0.])),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant(
|
||||
[10., 10., 0., 0., 5., 5., 0., 0., 0., 0., 0., 0., 0.])),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant(
|
||||
[10., 10., 0., 0., 5., 5., 0., 0., 0., 0., 0., 0., 0.])),
|
||||
)
|
||||
def test_preprocess_record_clipped(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
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(
|
||||
('binary_test_int', 2, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0.], [1., 0., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([1, 0, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([1., 0., 0., 0.], dtype=tf.float32),
|
||||
tf.ragged.constant([[1.], [1., 0., 0.],
|
||||
[1., 0., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
)
|
||||
def test_get_noised_result(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
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.named_parameters(
|
||||
('binary_test_int', 2, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('binary_test_float', 2, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0.], [5., 5., 0., 0.]])),
|
||||
('ternary_test_int', 3, tf.constant([10, 10, 0, 0], dtype=tf.int32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
('ternary_test_float', 3, tf.constant([10., 10., 0., 0.],
|
||||
dtype=tf.float32),
|
||||
tf.ragged.constant([[10.], [10., 0., 0.],
|
||||
[5., 5., 0., 0., 0., 0., 0., 0., 0.]])),
|
||||
)
|
||||
def test_get_noised_result_clipped(self, arity, record, expected_tree):
|
||||
query = tree_aggregation_query.DistributedTreeSumQuery(
|
||||
stddev=0., arity=arity)
|
||||
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)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
tf.test.main()
|
||||
|
|
|
|||
Loading…
Reference in a new issue