Modified-Code-Offset-construction-analysis/Extension to Modified Offset FE Analytics.py at main · CuriousVi17/Modified-Code-Offset-construction-analysis · GitHub

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from scipy.stats import binom
import numpy as np
import pandas as pd
import math

def binomial_cdf_sum(n, t, p):
    return binom.cdf(t, n, p)

def P_success_given_H(n, t, d_avg):
    return binomial_cdf_sum(n, t, d_avg)

def P_H1(n, t, p_bit):
    return binomial_cdf_sum(n, t, p_bit)

def P_success_same(n, t, p_bit, d_same, lam, t_lam, d_same_lam):
    P_H1_val = P_H1(n, t, p_bit)
    P_H2_val = 1 - P_H1_val
    P_S_H1 = P_success_given_H(n, t, d_same)
    P_S_H2 = P_success_given_H(lam, t_lam, d_same_lam)
    return P_S_H1 * P_H1_val + P_S_H2 * P_H2_val

def P_success_diff(n, t, p_bit, d_diff, lam, t_lam, d_diff_lam):
    P_H1_val = P_H1(n, t, p_bit)
    P_H2_val = 1 - P_H1_val
    P_S_H1 = P_success_given_H(n, t, d_diff)
    P_S_H2 = P_success_given_H(lam, t_lam, d_diff_lam)
    return P_S_H1 * P_H1_val + P_S_H2 * P_H2_val

# Parameters
m = 9
n = 2**m
s = 6
lam = 2**s
p_bit_same = 0.15
p_bit_diff = 0.5
d_same = 0.1
d_same_lam = 0.1
d_diff = 0.5
d_diff_lam = 0.5

t_range = range(4, 64, 4)
t_lam_range = range(2, 32, 1)

results = []

for t in t_range:
    for t_lam in t_lam_range:
        P_succ_same = P_success_same(n, t, p_bit_same, d_same, lam, t_lam, d_same_lam)
        P_succ_diff = P_success_diff(n, t, p_bit_diff, d_diff, lam, t_lam, d_diff_lam)
        FRR = 1 - P_succ_same
        FAR = P_succ_diff


        results.append({
            "t": t,
            "t_lam": t_lam,
            "P_succ_same": round(P_succ_same, 5),
            "FRR": round(FRR, 5),
            "FAR": round(FAR, 10),
            f"Bits of security": min(n-m*t, n - lam),
            "Bits of c_lam security": lam - s*t_lam,
            "Biometric bits of security": -round(math.log2(FAR),5)
        })

        FRR = 0
        FAR = 0


df = pd.DataFrame(results)
print(df.to_string(index=False))