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Ben
2022-06-24 14:19:10 +01:00
parent 6904ccfeff
commit 037afe0561
2 changed files with 26 additions and 23 deletions
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44
generate_data.py
View File

@@ -67,7 +67,7 @@ def get_security_level(est, dp=2):
def inequality(x, y):
""" A utility function which compresses the conditions x < y and x > y into a single condition via a multiplier
"""A utility function which compresses the conditions x < y and x > y into a single condition via a multiplier
:param x: the LHS of the inequality
:param y: the RHS of the inequality
"""
@@ -79,7 +79,7 @@ def inequality(x, y):
def automated_param_select_n(params, target_security=128):
""" A function used to generate the smallest value of n which allows for
"""A function used to generate the smallest value of n which allows for
target_security bits of security, for the input values of (params.Xe.stddev,params.q)
:param params: the standard deviation of the error
:param target_security: the target number of bits of security, 128 is default
@@ -92,8 +92,7 @@ def automated_param_select_n(params, target_security=128):
# get an estimate based on the prev. model
print("n = {}".format(params.n))
n_start = old_models(target_security, log2(
params.Xe.stddev), log2(params.q))
n_start = old_models(target_security, log2(params.Xe.stddev), log2(params.q))
# n_start = max(n_start, 450)
# TODO: think about throwing an error if the required n < 450
@@ -125,9 +124,9 @@ def automated_param_select_n(params, target_security=128):
print(
"the finalised parameters are n = {}, log2(sd) = {}, log2(q) = {}, with a security level of {}-bits".format(
params.n, log2(
params.Xe.stddev), log2(
params.q), security_level))
params.n, log2(params.Xe.stddev), log2(params.q), security_level
)
)
if security_level < target_security:
params.updated(n=None)
@@ -135,8 +134,9 @@ def automated_param_select_n(params, target_security=128):
return params, security_level
def generate_parameter_matrix(params_in, sd_range, target_security_levels=[
128], name="default_name"):
def generate_parameter_matrix(
params_in, sd_range, target_security_levels=[128], name="default_name"
):
"""
:param params_in: a initial set of LWE parameters
:param sd_range: a tuple (sd_min, sd_max) giving the values of sd for which to generate parameters
@@ -148,9 +148,10 @@ def generate_parameter_matrix(params_in, sd_range, target_security_levels=[
for lam in target_security_levels:
for sd in range(sd_min, sd_max + 1):
print("run for {}".format(lam, sd))
Xe_new = nd.NoiseDistribution.DiscreteGaussian(2**sd)
Xe_new = nd.NoiseDistribution.DiscreteGaussian(2 ** sd)
(params_out, sec) = automated_param_select_n(
params_in.updated(Xe=Xe_new), target_security=lam)
params_in.updated(Xe=Xe_new), target_security=lam
)
try:
results = load("{}.sobj".format(name))
@@ -159,14 +160,16 @@ def generate_parameter_matrix(params_in, sd_range, target_security_levels=[
results["{}".format(lam)] = []
results["{}".format(lam)].append(
(params_out.n, log2(params_out.q), log2(params_out.Xe.stddev), sec))
(params_out.n, log2(params_out.q), log2(params_out.Xe.stddev), sec)
)
save(results, "{}.sobj".format(name))
return results
def generate_zama_curves64(sd_range=[2, 58], target_security_levels=[
128], name="default_name"):
def generate_zama_curves64(
sd_range=[2, 58], target_security_levels=[128], name="default_name"
):
"""
The top level function which we use to run the experiment
@@ -174,15 +177,17 @@ def generate_zama_curves64(sd_range=[2, 58], target_security_levels=[
:param target_security_levels: a list of the target number of bits of security, 128 is default
:param name: a name to save the file
"""
if __name__ == '__main__':
if __name__ == "__main__":
D = ND.DiscreteGaussian
vals = range(sd_range[0], sd_range[1])
pool = multiprocessing.Pool(2)
init_params = LWE.Parameters(
n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(2 ** 55), m=oo, tag='params')
inputs = [(init_params, (val, val), target_security_levels, name)
for val in vals]
n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(2 ** 55), m=oo, tag="params"
)
inputs = [
(init_params, (val, val), target_security_levels, name) for val in vals
]
res = pool.starmap(generate_parameter_matrix, inputs)
return "done"
@@ -194,5 +199,4 @@ a = int(sys.argv[1])
b = int(sys.argv[2])
c = int(sys.argv[3])
# run the code
generate_zama_curves64(sd_range=(b, c), target_security_levels=[
a], name="{}".format(a))
generate_zama_curves64(sd_range=(b, c), target_security_levels=[a], name="{}".format(a))

5
verify_curves.py
View File

@@ -63,7 +63,7 @@ def verify_curve(security_level, a=None, b=None):
print(n_min)
print(n_max)
for n in range(n_max, n_min, - 1):
for n in range(n_max, n_min, -1):
model_sd = f_model(a, b, n)
table_sd = f_table(X["{}".format(security_level)], n)
print(n, table_sd, model_sd, model_sd >= table_sd)
@@ -92,6 +92,5 @@ def generate_and_verify(security_levels, log_q, name="verified_curves"):
return data
data = generate_and_verify(
[80, 96, 112, 128, 144, 160, 176, 192, 256], log_q=64)
data = generate_and_verify([80, 96, 112, 128, 144, 160, 176, 192, 256], log_q=64)
print(data)