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//
// Copyright 2020 Google LLC
//
// 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.
//
package com.google.privacy.differentialprivacy;
import com.google.privacy.differentialprivacy.proto.SummaryOuterClass.MechanismType;
import javax.annotation.Nullable;
/**
* Interface for primitives that add noise to numerical data, for use in differential privacy
* operations.
*/
public interface Noise {
double addNoise(
double x, int l0Sensitivity, double lInfSensitivity, double epsilon, @Nullable Double delta);
long addNoise(
long x, int l0Sensitivity, long lInfSensitivity, double epsilon, @Nullable Double delta);
ConfidenceInterval computeConfidenceInterval(
double noisedX,
int l0Sensitivity,
double lInfSensitivity,
double epsilon,
@Nullable Double delta,
double alpha);
ConfidenceInterval computeConfidenceInterval(
long noisedX,
int l0Sensitivity,
long lInfSensitivity,
double epsilon,
@Nullable Double delta,
double alpha);
MechanismType getMechanismType();
static double getL1Sensitivity(int l0Sensitivity, double lInfSensitivity) {
return l0Sensitivity * lInfSensitivity;
}
static double getL2Sensitivity(int l0Sensitivity, double lInfSensitivity) {
return Math.sqrt(l0Sensitivity) * lInfSensitivity;
}
/**
* Calculates a value k s.t. with probability {@code rank} the result of {@link #addNoise} with
* the given parameters will be less or equal to k.
*/
double computeQuantile(
double rank,
double x,
int l0Sensitivity,
double lInfSensitivity,
double epsilon,
@Nullable Double delta);
}