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add some params, check small sd bug

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Ben
2021-05-21 13:41:33 +01:00
parent 2227b9c499
commit 4618483117
1 changed files with 39 additions and 20 deletions
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59
scripts.py
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@@ -1,7 +1,9 @@
import estimator as est
import matplotlib.pyplot as plt
import numpy as np
from sage.stats.distributions.discrete_gaussian_lattice import DiscreteGaussianDistributionIntegerSampler
from concrete_params import concrete_LWE_params, concrete_RLWE_params
# easier to just load the estimator
load("estimator.py")
# define the four cost models used for Concrete (2 classical, 2 quantum)
# note that classical and quantum are the two models used in the "HE Std"
@@ -10,7 +12,7 @@ classical = lambda beta, d, B: ZZ(2) ** RR(0.292 * beta + 16.4 + log(8 * d, 2)),
quantum = lambda beta, d, B: ZZ(2) ** RR(0.265 * beta + 16.4 + log(8 * d, 2)),
classical_conservative = lambda beta, d, B: ZZ(2) ** RR(0.292 * beta),
quantum_conservative = lambda beta, d, B: ZZ(2) ** RR(0.265 * beta),
cost_models = [classical, quantum, classical_conservative, quantum_conservative]
cost_models = [classical, quantum, classical_conservative, quantum_conservative, BKZ.enum]
# functions to automate parameter selection
@@ -84,8 +86,11 @@ def get_all_security_levels(params):
m = oo
for model in cost_models:
model = model[0]
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
try:
model = model[0]
except:
model = model
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=model, m=oo)
results.append(get_security_level(estimate))
@@ -106,7 +111,7 @@ def inequality(x, y):
return -1
def automated_param_select_n(sd, n=None, q=2 ** 32, reduction_cost_model=est.BKZ.sieve, secret_distribution=(0, 1),
def automated_param_select_n(sd, n=None, q=2 ** 32, reduction_cost_model=BKZ.sieve, secret_distribution=(0, 1),
target_security=128):
""" A function used to generate the smallest value of n which allows for
target_security bits of security, for the input values of (sd,q)
@@ -131,13 +136,13 @@ def automated_param_select_n(sd, n=None, q=2 ** 32, reduction_cost_model=est.BKZ
# initial estimate, to determine if we are above or below the target security level
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo, skip = {"bkw","dec","arora-gb","mitm"})
security_level = get_security_level(estimate)
z = inequality(security_level, target_security)
while z * security_level < z * target_security:
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo, skip = {"bkw","dec","arora-gb","mitm"})
security_level = get_security_level(estimate)
@@ -148,7 +153,7 @@ def automated_param_select_n(sd, n=None, q=2 ** 32, reduction_cost_model=est.BKZ
return ZZ(n)
def automated_param_select_sd(n, sd=None, q=2 ** 32, reduction_cost_model=est.BKZ.sieve, secret_distribution=(0, 1),
def automated_param_select_sd(n, sd=None, q=2 ** 32, reduction_cost_model=BKZ.sieve, secret_distribution=(0, 1),
target_security=128):
""" A function used to generate the smallest value of sd which allows for
target_security bits of security, for the input values of (n,q)
@@ -172,7 +177,8 @@ def automated_param_select_sd(n, sd=None, q=2 ** 32, reduction_cost_model=est.BK
alpha = sqrt(2 * pi) * sd_ / RR(q)
# initial estimate, to determine if we are above or below the target security level
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
print("estimating for n, q, sd = {}".format(log(sd_,2)))
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo, skip = {"bkw","dec","arora-gb","mitm"})
security_level = get_security_level(estimate)
z = inequality(security_level, target_security)
@@ -181,17 +187,19 @@ def automated_param_select_sd(n, sd=None, q=2 ** 32, reduction_cost_model=est.BK
sd += z * 1
sd_ = 2 ** sd * q
alpha = sqrt(2 * pi) * sd_ / RR(q)
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
print("estimating for n, q, sd = {}".format(log(sd_,2)))
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo, skip = {"bkw","dec","arora-gb","mitm"})
security_level = get_security_level(estimate)
# final estimate (we went too far in the above loop)
sd -= z * 1
sd_ = 2 ** sd * q
alpha = sqrt(2 * pi) * sd_ / RR(q)
estimate = est.estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo)
security_level = get_security_level(estimate)
if security_level < target_security:
sd -= z * 1
sd_ = 2 ** sd * q
alpha = sqrt(2 * pi) * sd_ / RR(q)
estimate = estimate_lwe(n, alpha, q, secret_distribution=secret_distribution,
reduction_cost_model=reduction_cost_model, m=oo)
security_level = get_security_level(estimate)
print("the finalised parameters are n = {}, log2(sd) = {}, log2(q) = {}, with a security level of {}-bits".format(n,
sd,
@@ -202,7 +210,7 @@ def automated_param_select_sd(n, sd=None, q=2 ** 32, reduction_cost_model=est.BK
return sd
def generate_parameter_matrix(n_range, sd=None, q=2 ** 32, reduction_cost_model=est.BKZ.sieve,
def generate_parameter_matrix(n_range, sd=None, q=2 ** 32, reduction_cost_model=BKZ.sieve,
secret_distribution=(0, 1), target_security=128):
"""
:param n_range: a tuple (n_min, n_max) giving the values of n for which to generate parameters
@@ -233,6 +241,9 @@ def generate_parameter_matrix(n_range, sd=None, q=2 ** 32, reduction_cost_model=
secret_distribution=secret_distribution, target_security=target_security)
sd_ = sd
RESULTS.append((n, q, sd))
return RESULTS
@@ -242,6 +253,11 @@ def generate_parameter_step(results):
:param results: an output of generate_parameter_matrix
returns: a step plot of chosen parameters
EXAMPLE:
X = generate_parameter_matrix([700, 790])
generate_parameter_step(X)
plt.show()
"""
N = []
@@ -251,8 +267,7 @@ def generate_parameter_step(results):
N.append(n)
SD.append(sd)
plt.step(N, SD)
plt.show()
plt.scatter(N, SD)
return plt
@@ -287,4 +302,8 @@ def test_rounded_gaussian(sigma, number_samples):
# sort (values)
hist.sort(key=lambda x:x[0])
return hist
return hist