def test_anonymize_mean_with_budget_multiple(self):
expected_values = np.array([87.5219451, 437.4041544])
lower = 10.0
upper = 99.0
n = 100.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_mean_with_budget(
[87.0, 435.0], lower, upper, n, epsilon)
np.testing.assert_almost_equal(anonymized, expected_values)
def test_anonymize_mean_with_budget_single(self):
expected_value = 87.52194507326827
lower = 10.0
upper = 99.0
n = 100.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_mean_with_budget(
87.0, lower, upper, n, epsilon)
np.testing.assert_almost_equal(anonymized, expected_value)
def test_anonymize_mean_with_budget_single_many(self):
expected_values = np.array([87.5219451, 89.4041544, 86.5122696])
lower = 10.0
upper = 99.0
n = 100.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_mean_with_budget(
87.0, lower, upper, n, epsilon, size=3)
np.testing.assert_almost_equal(anonymized, expected_values)
def mean(cls, data, epsilon, axis=None):
"""
Performs the mean operation and anonymizes the value(s) using the provided
privacy budget.
Parameters
----------
data : list|ndarray
The data to retrieve the mean value(s) from.
epsilon : float
The privacy budget.
[axis] : int|tuple
Axis or tuple of axes along which to obtain the mean value(s).
Returns
-------
float|ndarray
The anonymized mean value(s).
"""
n = np.size(data, axis=axis)
lower = np.min(data, axis=axis)
upper = np.max(data, axis=axis)
value = np.mean(data, axis=axis)
if np.isscalar(lower):
anonymized = DiffPrivLaplaceMechanism.anonymize_mean_with_budget(
value, lower, upper, n, epsilon)
else:
size = lower.size
anonymized = np.zeros(size)
for index in range(0, size):
mean_value = DiffPrivLaplaceMechanism.anonymize_mean_with_budget(
value[index], lower[index], upper[index], n, epsilon)
anonymized[index] = mean_value
return anonymized