626_privacy/tensorflow_privacy
2
0
Fork 1
Code Issues Pull requests 1 Projects Releases Packages Wiki Activity Actions

Add clearer connection of TFP tree aggregation query and accounting arguments.

Browse source 
PiperOrigin-RevId: 425790945
This commit is contained in:
Zheng Xu 2022-02-01 21:34:11 -08:00 committed by A. Unique TensorFlower
parent 8a6827b27c
commit 6fde7b0480
2 changed files with 37 additions and 5 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

21
tensorflow_privacy/privacy/analysis/tree_aggregation_accountant.py
View file

@ -29,6 +29,10 @@ mechanism. Its public interface consists of the following methods:
min_separation: int,
orders: Union[float, Collection[float]]) -> Union[float, Collection[float]]:
computes RDP for DP-FTRL-NoTreeRestart.
compute_zcdp_single_tree(
noise_multiplier: float, total_steps: int, max_participation: int,
min_separation: int) -> Union[float, Collection[float]]:
computes zCDP for DP-FTRL-NoTreeRestart.
For RDP to (epsilon, delta)-DP conversion, use the following public function
described in `rdp_accountant.py`:
@ -36,6 +40,11 @@ described in `rdp_accountant.py`:
(or eps) given RDP at multiple orders and
a target value for eps (or delta).
The `noise_multiplier` is usually from `TreeCumulativeSumQuery` and
`TreeResidualSumQuery` in `dp_query.tree_aggregation_query`. The other
inputs depend on the data streaming setting (single/multi-pass) and the restart
strategy (see `restart_query`).
Example use:
(1) DP-FTRL-TreeRestart RDP:
@ -91,7 +100,9 @@ def compute_rdp_tree_restart(
Args:
noise_multiplier: A non-negative float representing the ratio of the
standard deviation of the Gaussian noise to the l2-sensitivity of the
function to which it is added.
function to which it is added, which is usually set in
`TreeCumulativeSumQuery` and `TreeResidualSumQuery` from
`dp_query.tree_aggregation_query`.
steps_list: A scalar or a list of non-negative intergers representing the
number of steps per epoch (between two restarts).
orders: An array (or a scalar) of RDP orders.
@ -282,7 +293,9 @@ def compute_rdp_single_tree(
Args:
noise_multiplier: A non-negative float representing the ratio of the
standard deviation of the Gaussian noise to the l2-sensitivity of the
function to which it is added.
function to which it is added, which is usually set in
`TreeCumulativeSumQuery` and `TreeResidualSumQuery` from
`dp_query.tree_aggregation_query`.
total_steps: Total number of steps (leaf nodes in tree aggregation).
max_participation: The maximum number of times a sample can appear.
min_separation: The minimum number of nodes between two appearance of a
@ -342,7 +355,9 @@ def compute_zcdp_single_tree(
Args:
noise_multiplier: A non-negative float representing the ratio of the
standard deviation of the Gaussian noise to the l2-sensitivity of the
function to which it is added.
function to which it is added, which is usually set in
`TreeCumulativeSumQuery` and `TreeResidualSumQuery` from
`dp_query.tree_aggregation_query`.
total_steps: Total number of steps (leaf nodes in tree aggregation).
max_participation: The maximum number of times a sample can appear.
min_separation: The minimum number of nodes between two appearance of a

21
tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py
View file

@ -18,6 +18,19 @@ 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 core
logic of tree aggregation is implemented in `tree_aggregation.TreeAggregator`
and `tree_aggregation.EfficientTreeAggregator`.
Depending on the data streaming setting (single/multi-pass), the privacy
accounting method ((epsilon,delta)-DP/RDP/zCDP), and the restart strategy (see
`restart_query`), the DP bound can be computed by one of the public methods
in `analysis.tree_aggregation_accountant`.
For example, for a single-pass algorithm where a sample may appear at most once
in the querying process; if `get_noised_result` is called `steps` times, the
corresponding epsilon for a `target_delta` and `noise_multiplier` to achieve
(epsilon,delta)-DP can be computed as:
orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
rdp = compute_rdp_tree_restart(noise_multiplier, [steps], orders)
eps = rdp_accountant.get_privacy_spent(orders, rdp, target_delta)[0]
"""
import attr
@ -210,7 +223,9 @@ class TreeCumulativeSumQuery(dp_query.SumAggregationDPQuery):
clip_norm: Each record will be clipped so that it has L2 norm at most
`clip_norm`.
noise_multiplier: The effective noise multiplier for the sum of records.
Noise standard deviation is `clip_norm*noise_multiplier`.
Noise standard deviation is `clip_norm*noise_multiplier`. The value can
be used as the input of the privacy accounting functions in
`analysis.tree_aggregation_accountant`.
record_specs: A nested structure of `tf.TensorSpec`s specifying structure
and shapes of records.
noise_seed: Integer seed for the Gaussian noise generator. If `None`, a
@ -461,7 +476,9 @@ class TreeResidualSumQuery(dp_query.SumAggregationDPQuery):
clip_norm: Each record will be clipped so that it has L2 norm at most
`clip_norm`.
noise_multiplier: The effective noise multiplier for the sum of records.
Noise standard deviation is `clip_norm*noise_multiplier`.
Noise standard deviation is `clip_norm*noise_multiplier`. The value can
be used as the input of the privacy accounting functions in
`analysis.tree_aggregation_accountant`.
record_specs: A nested structure of `tf.TensorSpec`s specifying structure
and shapes of records.
noise_seed: Integer seed for the Gaussian noise generator. If `None`, a