def test_anonymize_min_with_budget_multiple(self):
expected_values = np.array([87.5864551, 437.701297])
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_min_with_budget(
[87.0, 435.0], epsilon)
np.testing.assert_almost_equal(anonymized, expected_values)
def test_anonymize_min_with_budget_single(self):
expected_value = 87.58645513850368
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_min_with_budget(
87.0, epsilon)
np.testing.assert_almost_equal(anonymized, expected_value)
def test_anonymize_min_with_budget_single_many(self):
expected_values = np.array([87.5864551, 89.701297, 86.4519884])
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_min_with_budget(
87.0, epsilon, size=3)
np.testing.assert_almost_equal(anonymized, expected_values)
def min(cls, data, epsilon, axis=None):
"""
Performs the min operation and anonymizes the value(s) using the provided
privacy budget.
Parameters
----------
data : list|ndarray
The data to retrieve the min value(s) from.
epsilon : float
The privacy budget.
[axis] : int|tuple
Axis or tuple of axes along which to obtain the min value(s).
Returns
-------
float|ndarray
The anonymized min value(s).
"""
value = np.min(data, axis=axis)
anonymized = DiffPrivLaplaceMechanism.anonymize_min_with_budget(
value, epsilon)
return anonymized