# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from typing import Dict, List, Tuple
from nvflare.app_common.app_constant import StatisticsConstants as StC
[docs]class StatisticsPrivacyCleanser(ABC):
[docs] @abstractmethod
def apply(self, statistics: dict, client_name: str) -> Tuple[dict, bool]:
pass
[docs] def cleanse(
self, statistics: dict, statistic_keys: List[str], validation_result: Dict[str, Dict[str, bool]]
) -> (dict, bool):
"""
Args:
statistics: original client local metrics
statistic_keys: statistic keys need to be cleansed
validation_result: local metrics privacy validation result
Returns:
filtered metrics with feature metrics that violating the privacy policy be removed from the original metrics
"""
statistics_modified = False
for key in statistic_keys:
if key != StC.STATS_COUNT:
for ds_name in list(statistics[key].keys()):
for feature in list(statistics[key][ds_name].keys()):
if not validation_result[ds_name][feature]:
statistics[key][ds_name].pop(feature, None)
statistics_modified = True
return statistics, statistics_modified