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scope_vs_performance.py
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scope_vs_performance.py
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from random import randint
import csv
import time
import subprocess
import sys
import multiprocessing
import matplotlib.pyplot as plt
import numpy as np
# Lists to store computed values for analysis and plotting
average_compute_times = []
average_scope_values = []
def rsa_encrypt_decrypt(M, key):
c = pow(M, key[0]) % key[1]
return c
def is_prime(num):
if num <= 1:
return False
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
return False
return True
def run_protocol(I, J, r1, r2, e, n, d):
start = time.perf_counter()
bob_pub_key = [e, n]
bob_priv_key = [d, n]
N = n.bit_length()
X = randint(0, n)
c = rsa_encrypt_decrypt(X, bob_pub_key)
c = c - I
scope = r2 + r1
n_values = [0] * (scope + 1)
for i in range(1, len(n_values)):
n_values[i] = rsa_encrypt_decrypt(c + i, bob_priv_key)
m = n_values[1:]
N = max(m).bit_length()
prime_list = [i for i in range(2, 2 ** (N - 1)) if is_prime(i)]
Z = [0] * (scope)
prime = prime_list.pop()
for i in range(0, len(m)):
Z[i] = m[i] % prime
is_bool = False
while not is_bool:
prime_found = True
for i in range(len(m)):
for j in range(i + 1, len(m)):
if abs(Z[i] - Z[j]) < 2 or not (0 < Z[i] < prime - 1):
prime_found = False
break
if not prime_found:
break
if prime_found:
is_bool = True
elif prime_list:
prime = prime_list.pop()
Z = [val % prime for val in m]
else:
break
for i in range(J, len(Z)):
Z[i] = (Z[i] + 1) % prime
output = X % prime != Z[I - 1]
correct = output == (I > J)
compute_time = time.perf_counter() - start
return I, J, output, correct, compute_time, scope
def process_iteration(args):
I, J, r1, r2, e, n, d = args
result = run_protocol(I, J, r1, r2, e, n, d)
return result
def analyze_results(csv_file):
total_compute_time = 0
total_scope = 0
num_correct = 0
num_total = 0
with open(csv_file, mode="r") as file:
reader = csv.DictReader(file)
for row in reader:
total_compute_time += float(row["Compute Time"])
total_scope += int(row["Scope"])
num_total += 1
if row["Correct"] == "True":
num_correct += 1
if num_total > 0:
average_compute_time = total_compute_time / num_total
average_scope = total_scope / num_total
else:
average_compute_time = 0
average_scope = 0
average_compute_times.append(average_compute_time)
average_scope_values.append(average_scope)
def plot(average_scope_values, average_compute_times): # Switched the arguments
plt.scatter(average_scope_values, average_compute_times, c='blue', marker='o') # Switched arguments here
plt.xlabel('Average Scope (I & J range)')
plt.ylabel('Average Compute Time (seconds)') # Switched the labels
plt.title('Average Scope vs Average Compute Time')
plt.xlim(0, 1000000)
plt.ylim(0, max(average_compute_times)+.1*max(average_compute_times)) # Adjusted the limits
plt.grid(True)
# Calculate the line of best fit
fit = np.polyfit(average_scope_values, average_compute_times, 1)
fit_fn = np.poly1d(fit)
# Generate x values for the line of best fit
x_values = np.linspace(min(average_scope_values), max(average_scope_values), 100)
# Plot the line of best fit
plt.plot(x_values, fit_fn(x_values), 'r--', label='Line of Best Fit')
plt.legend(loc='upper left')
plt.show()
if __name__ == '__main__':
num_repeats = 1000
for _ in range(num_repeats):
e = 17
n = 3233
d = 413
num_iterations = 10
rand = randint(0, 999999)
csv_file = f"protocol_results~{str(rand)}.csv"
pool = multiprocessing.Pool()
with open(csv_file, mode="w", newline="") as file:
writer = csv.writer(file)
writer.writerow(["I", "J", "Output", "Correct", "Compute Time", "Scope"])
iteration_args = []
for _ in range(num_iterations):
I = randint(1, 500000)
J = randint(1, 500000)
r1 = min(I, J)
r2 = max(I, J)
iteration_args.append((I, J, r1, r2, e, n, d))
results = pool.map(process_iteration, iteration_args)
for result in results:
writer.writerow(result)
pool.close()
pool.join()
analyze_results(csv_file)
plot(average_scope_values, average_compute_times)