def sum(cls, data, epsilon, axis=None):
"""
Performs the sum operation and anonymizes the value(s) using the provided
privacy budget.
Parameters
----------
data : list|ndarray
The data to retrieve the sum value(s) from.
epsilon : float
The privacy budget.
[axis] : int|tuple
Axis or tuple of axes along which to obtain the sum value(s).
Returns
-------
float|ndarray
The anonymized sum value(s).
"""
lower = np.min(data, axis=axis)
upper = np.max(data, axis=axis)
value = np.sum(data, axis=axis)
if np.isscalar(lower):
anonymized = DiffPrivLaplaceMechanism.anonymize_sum_with_budget(
value, lower, upper, epsilon)
else:
size = lower.size
anonymized = np.zeros(size)
for index in range(0, size):
anonymized[
index] = DiffPrivLaplaceMechanism.anonymize_sum_with_budget(
value[index], lower[index], upper[index], epsilon)
return anonymized
def test_anonymize_sum_with_budget_multiple(self):
expected_values = np.array([145.0590587, 702.4284063])
lower = 10.0
upper = 99.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_sum_with_budget(
[87.0, 435.0], lower, upper, epsilon)
np.testing.assert_almost_equal(anonymized, expected_values)
def test_anonymize_sum_with_budget_single_many(self):
expected_values = np.array([145.0590587, 354.4284063, 32.746848])
lower = 10.0
upper = 99.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_sum_with_budget(
87.0, lower, upper, epsilon, size=3)
np.testing.assert_almost_equal(anonymized, expected_values)
def test_anonymize_sum_with_budget_single(self):
expected_value = 145.05905871186388
lower = 10.0
upper = 99.0
epsilon = 1.0
self.set_seed()
anonymized = DiffPrivLaplaceMechanism.anonymize_sum_with_budget(
87.0, lower, upper, epsilon)
np.testing.assert_almost_equal(anonymized, expected_value)