def test_compute_dp_count(self):
params = dp_computations.MeanVarParams(
eps=0.5,
delta=1e-10,
min_value=0,
max_value=0,
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
# Laplace Mechanism
l1_sensitivity = dp_computations.compute_l1_sensitivity(
l0_sensitivity, linf_sensitivity)
results = [
dp_computations.compute_dp_count(count=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_count(count=10, dp_params=params)
for _ in range(N_ITERATIONS)
]
self._test_gaussian_noise(results, 10, params.eps, params.delta,
l2_sensitivity)
def compute_metrics(self, accumulator: AccumulatorType) -> dict:
return {
'privacy_id_count':
dp_computations.compute_dp_count(accumulator,
self._params.scalar_noise_params)
}
def compute_metrics(self) -> float:
return dp_computations.compute_dp_count(self._count,
self._params.mean_var_params)
def compute_metrics(self, count: AccumulatorType) -> dict:
return {
'count':
dp_computations.compute_dp_count(count,
self._params.scalar_noise_params)
}