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binary.py
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# MIT License
#
# Copyright (C) IBM Corporation 2019
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
The binary mechanism for differential privacy.
"""
import numpy as np
from diffprivlib.mechanisms.base import DPMechanism
from diffprivlib.utils import copy_docstring
class Binary(DPMechanism):
r"""The classic binary mechanism in differential privacy.
Given a binary input value, the mechanism randomly decides to flip to the other binary value or not, in order to
satisfy differential privacy.
Paper link: https://arxiv.org/pdf/1612.05568.pdf
Parameters
----------
epsilon : float
Privacy parameter :math:`\epsilon` for the mechanism. Must be in [0, ∞].
value0 : str
0th binary label.
value1 : str
1st binary label. Cannot be the same as ``value0``.
random_state : int or RandomState, optional
Controls the randomness of the mechanism. To obtain a deterministic behaviour during randomisation,
``random_state`` has to be fixed to an integer.
Notes
-----
* The binary attributes, known as `labels`, must be specified as strings. If non-string labels are required (e.g.
integer-valued labels), a :class:`.DPTransformer` can be used (e.g. :class:`.IntToString`).
"""
def __init__(self, *, epsilon, value0, value1, random_state=None):
super().__init__(epsilon=epsilon, delta=0.0, random_state=random_state)
self.value0, self.value1 = self._check_labels(value0, value1)
@classmethod
def _check_labels(cls, value0, value1):
if not isinstance(value0, str) or not isinstance(value1, str):
raise TypeError("Binary labels must be strings. Use a DPTransformer (e.g. transformers.IntToString) for "
"non-string labels")
if len(value0) * len(value1) == 0:
raise ValueError("Binary labels must be non-empty strings")
if value0 == value1:
raise ValueError("Binary labels must not match")
return value0, value1
def _check_all(self, value):
super()._check_all(value)
self._check_labels(self.value0, self.value1)
if not isinstance(value, str):
raise TypeError("Value to be randomised must be a string")
if value not in [self.value0, self.value1]:
raise ValueError(f"Value to be randomised is not in the domain {{\"{self.value0}\", \"{self.value1}\"}}, "
f"got \"{value}\".")
return True
@copy_docstring(DPMechanism.bias)
def bias(self, value):
raise NotImplementedError
@copy_docstring(DPMechanism.variance)
def variance(self, value):
raise NotImplementedError
def randomise(self, value):
"""Randomise `value` with the mechanism.
Parameters
----------
value : str
The value to be randomised.
Returns
-------
str
The randomised value.
"""
self._check_all(value)
indicator = 0 if value == self.value0 else 1
unif_rv = self._rng.random() * (np.exp(self.epsilon) + 1)
if unif_rv > np.exp(self.epsilon) + self.delta:
indicator = 1 - indicator
return self.value1 if indicator else self.value0