github/concrete
1
1
Fork 0
mirror of https://github.com/zama-ai/concrete.git synced 2026-02-15 07:05:09 -05:00
Code Issues Packages Projects Releases Wiki Activity

uypdates for aws

Browse Source
This commit is contained in:
Ben
2022-04-12 14:03:22 +01:00
parent 468dcfc1f3
commit 85f05ea661
1 changed files with 15 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
new_scripts.py
View File

@@ -1,6 +1,9 @@
import gc
from estimator_new import *
from sage.all import oo, save
from math import log2
import gc
def old_models(security_level, sd, logq = 32):
@@ -35,7 +38,6 @@ def old_models(security_level, sd, logq = 32):
return round(n_est)
def estimate(params, red_cost_model = RC.BDGL16):
"""
Retrieve an estimate using the Lattice Estimator, for a given set of input parameters
@@ -45,7 +47,6 @@ def estimate(params, red_cost_model = RC.BDGL16):
est = LWE.estimate(params, deny_list=("arora-gb", "bkw"), red_cost_model=red_cost_model)
return est
def get_security_level(est, dp = 2):
"""
Get the security level lambda from a Lattice Estimator output
@@ -59,7 +60,6 @@ def get_security_level(est, dp = 2):
security_level = round(log2(min(attack_costs)), dp)
return security_level
def inequality(x, y):
""" 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
@@ -71,7 +71,6 @@ def inequality(x, y):
if x > y:
return -1
def automated_param_select_n(params, 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 (params.Xe.stddev,params.q)
@@ -109,9 +108,6 @@ def automated_param_select_n(params, target_security=128):
# if params.n > 1024:
# we only need to consider powers-of-two in this case
# TODO: fill in this case! For n > 1024 we only need to consider every 256
params = params.updated(n = params.n + z * 8)
costs = estimate(params)
security_level = get_security_level(costs, 2)
@@ -137,6 +133,10 @@ def automated_param_select_n(params, target_security=128):
if security_level < target_security:
params.updated(n=None)
del(costs)
del(costs2)
gc.collect()
return params
def generate_parameter_matrix(params_in, sd_range, target_security_levels=[128], name="v0.sobj"):
@@ -162,11 +162,11 @@ def generate_parameter_matrix(params_in, sd_range, target_security_levels=[128],
params_out = automated_param_select_n(params_in.updated(Xe=Xe_new), target_security=lam)
results["{}".format(lam)].append((params_out.n, params_out.q, params_out.Xe.stddev))
save(results, "{}.sobj".format(name))
del(params_out)
gc.collect()
return results
def generate_zama_curves64(sd_range=[2, 60], target_security_levels=[256], name="v0256.sobj"):
def generate_zama_curves64(sd_range=[25, 26], target_security_levels=[256], name="v0256.sobj"):
D = ND.DiscreteGaussian
init_params = LWE.Parameters(n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(131072.00), m=oo, tag='TFHE_DEFAULT')
@@ -182,5 +182,10 @@ import sys
a = int(sys.argv[1])
print(a)
D = ND.DiscreteGaussian
init_params = LWE.Parameters(n=1024, q=2 ** 64, Xs=ND.UniformMod(2), Xe=D(131072.00), m=oo, tag='TFHE_DEFAULT')
generate_zama_curves64(target_security_levels=[a], name="{}".format(a))