def _windsorized(self, epsilon, lower_bounds, higher_bounds, output):
"""
Privatize each dimension of the output in a winsorized manner
"""
if isiterable(output[0]):
noise = []
estimate = []
for index in range(len(output)):
e, n = self._windsorized(epsilon / len(output), lower_bounds[index], higher_bounds[index], output[index])
estimate.append(e)
noise.append(n)
return estimate, noise
dimension = list(output)
rad = len(output) ** (1.0 / 3 + 0.1)
lps = dpalgos.estimate_percentile(0.25, dimension,
epsilon / 4,
lower_bounds,
higher_bounds)
hps = dpalgos.estimate_percentile(0.75, dimension,
epsilon / 4,
lower_bounds,
higher_bounds)
crude_mu = float(lps + hps) / 2
crude_iqr = abs(hps - lps)
u = crude_mu + 4 * rad * crude_iqr
l = crude_mu - 4 * rad * crude_iqr
# Compute windsorized mean for range
self._sanitize_multidim(dimension, [l] * len(dimension), [u] * len(dimension))
mean_estimate = float(sum(dimension)) / len(dimension)
noise = dpalgos.gen_noise(self.sensitivity_factor * float(abs(u - l)) / (2 * epsilon * len(dimension)))
return mean_estimate, noise
def _windsorized(self, epsilon, lower_bounds, higher_bounds, output):
"""
Privatize each dimension of the output in a winsorized manner
"""
if isiterable(output[0]):
noise = []
estimate = []
for index in range(len(output)):
e, n = self._windsorized(epsilon / len(output),
lower_bounds[index],
higher_bounds[index], output[index])
estimate.append(e)
noise.append(n)
return estimate, noise
dimension = list(output)
rad = len(output)**(1.0 / 3 + 0.1)
lps = dpalgos.estimate_percentile(0.25, dimension, epsilon / 4,
lower_bounds, higher_bounds)
hps = dpalgos.estimate_percentile(0.75, dimension, epsilon / 4,
lower_bounds, higher_bounds)
crude_mu = float(lps + hps) / 2
crude_iqr = abs(hps - lps)
u = crude_mu + 4 * rad * crude_iqr
l = crude_mu - 4 * rad * crude_iqr
# Compute windsorized mean for range
self._sanitize_multidim(dimension, [l] * len(dimension),
[u] * len(dimension))
mean_estimate = float(sum(dimension)) / len(dimension)
noise = dpalgos.gen_noise(self.sensitivity_factor * float(abs(u - l)) /
(2 * epsilon * len(dimension)))
return mean_estimate, noise
def _perturb(self, bound_ranges, epsilon):
if not isiterable(bound_ranges):
return dpalgos.gen_noise(self.sensitivity_factor *
float(bound_ranges) / epsilon)
return [
self._perturb(br, epsilon / len(bound_ranges))
for br in bound_ranges
]
def _perturb(self, bound_ranges, epsilon):
if not isiterable(bound_ranges):
return dpalgos.gen_noise(
self.sensitivity_factor * float(bound_ranges) / epsilon)
return [
self._perturb(br, epsilon / len(bound_ranges))
for br in bound_ranges
]
