def self_compose(
self,
num_times: int,
tail_mass_truncation: float = 1e-15) -> 'PrivacyLossDistribution':
"""Computes PLD resulting from repeated composing the PLD with itself.
Args:
num_times: the number of times to compose this PLD with itself.
tail_mass_truncation: an upper bound on the tails of the probability mass
of the PLD that might be truncated. Currently only supports for
pessimistic estimates.
Returns:
A privacy loss distribution which is the result of the composition.
"""
if not self.pessimistic_estimate:
# Currently support truncation only for pessimistic estimates.
tail_mass_truncation = 0
new_rounded_probability_mass_function = common.self_convolve_dictionary(
self.rounded_probability_mass_function,
num_times,
tail_mass_truncation=tail_mass_truncation)
new_infinity_mass = (1 - ((1 - self.infinity_mass)**num_times))
new_infinity_mass += tail_mass_truncation
return PrivacyLossDistribution(
new_rounded_probability_mass_function,
self.value_discretization_interval,
new_infinity_mass,
pessimistic_estimate=self.pessimistic_estimate)
def test_self_convolve_dictionary(self):
inp_dictionary = {1: 2, 3: 5, 4: 6}
expected_result = {
3: 8,
5: 60,
6: 72,
7: 150,
8: 360,
9: 341,
10: 450,
11: 540,
12: 216
}
result = common.self_convolve_dictionary(inp_dictionary, 3)
test_util.dictionary_almost_equal(self, expected_result, result)
def self_compose(self, num_times: int) -> 'PrivacyLossDistribution':
"""Computes PLD resulting from repeated composing the PLD with itself.
Args:
num_times: the number of times to compose this PLD with itself.
Returns:
A privacy loss distribution which is the result of the composition.
"""
new_rounded_probability_mass_function = common.self_convolve_dictionary(
self.rounded_probability_mass_function, num_times)
new_infinity_mass = (1 - ((1 - self.infinity_mass)**num_times))
return PrivacyLossDistribution(new_rounded_probability_mass_function,
self.value_discretization_interval,
new_infinity_mass)