def test_compute_dp_sum(self):
params = dp_computations.MeanVarParams(
eps=0.5,
delta=1e-10,
min_value=2,
max_value=3,
max_partitions_contributed=1,
max_contributions_per_partition=1,
noise_kind=NoiseKind.LAPLACE)
l0_sensitivity = params.l0_sensitivity()
linf_sensitivity = params.max_contributions_per_partition * max(
params.min_value, params.max_value)
# Laplace Mechanism
l1_sensitivity = dp_computations.compute_l1_sensitivity(
l0_sensitivity, linf_sensitivity)
results = [
dp_computations.compute_dp_sum(sum=10, dp_params=params)
for _ in range(N_ITERATIONS)
]
self._test_laplace_noise(results, 10, params.eps, l1_sensitivity)
# Gaussian Mechanism
params.noise_kind = NoiseKind.GAUSSIAN
l2_sensitivity = dp_computations.compute_l2_sensitivity(
l0_sensitivity, linf_sensitivity)
results = [
dp_computations.compute_dp_sum(sum=10, dp_params=params)
for _ in range(N_ITERATIONS)
]
self._test_gaussian_noise(results, 10, params.eps, params.delta,
l2_sensitivity)
def test_compute_dp_sum(self, bound_per_partition):
min_value = max_value = min_sum_per_partition = max_sum_per_partition = None
if bound_per_partition:
min_sum_per_partition, max_sum_per_partition = 2, 3
else:
min_value, max_value = 2, 3
params = dp_computations.ScalarNoiseParams(
eps=0.5,
delta=1e-10,
min_value=min_value,
max_value=max_value,
min_sum_per_partition=min_sum_per_partition,
max_sum_per_partition=max_sum_per_partition,
max_partitions_contributed=1,
max_contributions_per_partition=1,
noise_kind=NoiseKind.LAPLACE)
l0_sensitivity = params.l0_sensitivity()
if bound_per_partition:
linf_sensitivity = params.max_contributions_per_partition * max(
params.min_sum_per_partition, params.max_sum_per_partition)
else: # bound per contribution
linf_sensitivity = params.max_contributions_per_partition * max(
params.min_value, params.max_value)
# Laplace Mechanism
l1_sensitivity = dp_computations.compute_l1_sensitivity(
l0_sensitivity, linf_sensitivity)
results = [
dp_computations.compute_dp_sum(sum=10, dp_params=params)
for _ in range(N_ITERATIONS)
]
self._test_laplace_noise(results=results,
num_trials=N_ITERATIONS,
expected_mean=10,
eps=params.eps,
l1_sensitivity=l1_sensitivity)
# Gaussian Mechanism
params.noise_kind = NoiseKind.GAUSSIAN
l2_sensitivity = dp_computations.compute_l2_sensitivity(
l0_sensitivity, linf_sensitivity)
results = [
dp_computations.compute_dp_sum(sum=10, dp_params=params)
for _ in range(N_ITERATIONS)
]
self._test_gaussian_noise(results=results,
expected_mean=10,
num_trials=N_ITERATIONS,
eps=params.eps,
delta=params.delta,
l2_sensitivity=l2_sensitivity)
def test_compute_dp_sum_min_max_zero(self):
params = dp_computations.ScalarNoiseParams(
eps=0.5,
delta=1e-10,
min_value=0,
max_value=0,
min_sum_per_partition=None,
max_sum_per_partition=None,
max_partitions_contributed=1,
max_contributions_per_partition=1,
noise_kind=NoiseKind.LAPLACE)
self.assertEqual(0, dp_computations.compute_dp_sum(10, params))
def compute_metrics(self) -> float:
return dp_computations.compute_dp_sum(self._sum,
self._params.mean_var_params)
def compute_metrics(self, sum_: AccumulatorType) -> dict:
return {
'sum':
dp_computations.compute_dp_sum(sum_,
self._params.scalar_noise_params)
}
def compute_metrics(self, sum: AccumulatorType) -> dict:
return {
'sum':
dp_computations.compute_dp_sum(sum, self._params.mean_var_params)
}