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start the search using the most recent models

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Ben
2022-04-05 21:47:33 +02:00
parent 0b7fbdd6cb
commit 289276a59f
1 changed files with 31 additions and 17 deletions
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48
new_scripts.py
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@@ -1,28 +1,34 @@
from estimator_new import *
from math import log2
def old_models(security_level, n):
def old_models(security_level, sd, logq = 32):
"""
Use the old model as a starting point for the data gathering step
TODO: update this and integrate a flag for it
"""
def evaluate_model(sd, a, b):
return (sd - b)/a
models = dict()
models["80"] = 0
models["96"] = 0
models["112"] = 0
models["128"] = 0
models["144"] = 0
models["160"] = 0
models["176"] = 0
models["192"] = 0
models["208"] = 0
models["224"] = 0
models["240"] = 0
models["256"] = 0
models["80"] = (-0.04049295502947623, 1.1288318226557081 + logq)
models["96"] = (-0.03416314056943681, 1.4704806061716345 + logq)
models["112"] = (-0.02970984362676178, 1.7848907787798667 + logq)
models["128"] = (-0.026361288425133814, 2.0014671315214696 + logq)
models["144"] = (-0.023744534465622812, 2.1710601038230712 + logq)
models["160"] = (-0.021667220727651954, 2.3565507936475476 + logq)
models["176"] = (-0.019947662046189942, 2.5109588704235803 + logq)
models["192"] = (-0.018552804646747204, 2.7168913723130816 + logq)
models["208"] = (-0.017291091126923574, 2.7956961446214326 + logq)
models["224"] = (-0.016257546811508806, 2.9582401000615226 + logq)
models["240"] = (-0.015329741032015766, 3.0744579055889782 + logq)
models["256"] = (-0.014530554319171845, 3.2094375376751745 + logq)
return 0
(a, b) = models["{}".format(security_level)]
n_est = evaluate_model(sd, a, b)
return round(n_est)
def estimate(params):
@@ -74,11 +80,19 @@ def automated_param_select_n(params, target_security=128):
"""
# get an initial estimate
costs = estimate(params)
security_level = get_security_level(costs, 2)
# costs = estimate(params)
# security_level = get_security_level(costs, 2)
# determine if we are above or below the target security level
# z = inequality(security_level, target_security)
# get an estimate based on the prev. model
n_start = old_models(target_security, log2(params.Xe.stddev))
params = params.updated(n=n_start)
costs2 = estimate(params)
security_level = get_security_level(costs2, 2)
z = inequality(security_level, target_security)
# we keep n > 2 * target_security as a rough baseline for mitm security (on binary key guessing)
while z * security_level < z * target_security and params.n > 2 * target_security:
params = params.updated(n = params.n + z * 8)
@@ -145,7 +159,7 @@ def test_it():
# print(y)
#z1 = automated_param_select_n(schemes.TFHE630.updated(n=786), 128)
#print(z1)
# sd_range = [1,4]
sd_range = [1,4]
z3 = generate_parameter_matrix(schemes.TFHE630, sd_range=[17,19], target_security_levels=[128, 192, 256])
print(z3)