def test_geometric_mechanism_respects_random_state(self):
lm0 = noisers.GeometricMechanism(
lambda x: np.array([1., 2., 3.]),
1.,
2.,
random_state=np.random.RandomState(seed=125))
result0 = lm0(5.)
lm1 = noisers.GeometricMechanism(
lambda x: np.array([1., 2., 3.]),
1.,
2.,
random_state=np.random.RandomState(seed=125))
result1 = lm1(5.)
self.assertAlmostEqual(list(result0), list(result1))
result2 = lm1(5.)
self.assertNotEqual(list(result1), list(result2))
def test_geometric_mechanism_adds_expected_noise(self):
rs = FakeGeometricRandomState(np.array([[3., 6., 13.], [1., 2., 7.]]))
lm = noisers.GeometricMechanism(lambda x: np.array([1., 2., 3.]),
1.,
2.,
random_state=rs)
result = lm(5.)
np.testing.assert_array_equal(result, np.array([3., 6., 9.]))
def __init__(self, epsilon, random_state=None):
"""Instantiates a GeometricEstimateNoiser object.
Args:
epsilon: The differential privacy level.
random_state: Optional instance of numpy.random.RandomState that is used
to seed the random number generator.
"""
# Note that any cardinality estimator will have sensitivity (delta_f) of 1.
self._noiser = noisers.GeometricMechanism(lambda x: x, 1.0, epsilon,
random_state)
def test_geometric_mechanism_works_with_scipy_stats_geom(self):
lm = noisers.GeometricMechanism(lambda x: np.array([1., 2., 3.]), 1.,
2.)
result = lm(5.)
self.assertLen(result, 3)
