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 _get_data_bounds(self, records, epsilon):
"""
Generate the output bounds for the given data set for a pre
defined computation
"""
compute_driver = self.compute_driver_class()
min_vals, max_vals = self.data_driver.min_bounds, self.data_driver.max_bounds
sensitive = self.data_driver.sensitiveness
# Find the first and third quartile of the distribution in a
# differentially private manner
records_transpose = zip(*records)
hist = dpalgos.histogram(records_transpose, sensitive, epsilon)
logger.debug("Ask compute driver what percentile to calculate")
percentile_values = compute_driver.get_percentiles(hist)
logger.debug("Estimating percentiles")
lower_percentiles = []
higher_percentiles = []
for index in range(len(records_transpose)):
if not sensitive[index]:
lower_percentiles.append(0)
higher_percentiles.append(0)
else:
lp = dpalgos.estimate_percentile(percentile_values[index][0],
records_transpose[index],
epsilon / (3 * len(records_transpose)),
min_vals[index],
max_vals[index])
hp = dpalgos.estimate_percentile(percentile_values[index][1],
records_transpose[index],
epsilon / (3 * len(records_transpose)),
min_vals[index],
max_vals[index])
lower_percentiles.append(lp)
higher_percentiles.append(hp)
logger.debug("Finished percentile estimation")
logger.debug("Output bound estimation in progress")
# Use the ComputeDriver's bound generator to generate the
# output bounds
return compute_driver.get_output_bounds(lower_percentiles,
higher_percentiles)
def _get_data_bounds(self, records, epsilon):
"""
Generate the output bounds for the given data set for a pre
defined computation
"""
compute_driver = self.compute_driver_class()
min_vals, max_vals = self.data_driver.min_bounds, self.data_driver.max_bounds
sensitive = self.data_driver.sensitiveness
# Find the first and third quartile of the distribution in a
# differentially private manner
records_transpose = zip(*records)
hist = dpalgos.histogram(records_transpose, sensitive, epsilon)
logger.debug("Ask compute driver what percentile to calculate")
percentile_values = compute_driver.get_percentiles(hist)
logger.debug("Estimating percentiles")
lower_percentiles = []
higher_percentiles = []
for index in range(len(records_transpose)):
if not sensitive[index]:
lower_percentiles.append(0)
higher_percentiles.append(0)
else:
lp = dpalgos.estimate_percentile(
percentile_values[index][0], records_transpose[index],
epsilon / (3 * len(records_transpose)), min_vals[index],
max_vals[index])
hp = dpalgos.estimate_percentile(
percentile_values[index][1], records_transpose[index],
epsilon / (3 * len(records_transpose)), min_vals[index],
max_vals[index])
lower_percentiles.append(lp)
higher_percentiles.append(hp)
logger.debug("Finished percentile estimation")
logger.debug("Output bound estimation in progress")
# Use the ComputeDriver's bound generator to generate the
# output bounds
return compute_driver.get_output_bounds(lower_percentiles,
higher_percentiles)
