ntru_cantor_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import string\n",
    "import time\n",
    "from NtruEncrypt import *\n",
    "from Polynomial import Zx\n",
    "from num_to_polynomial import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_string(l):\n",
    "    m = ''.join([random.choice(string.ascii_letters + string.digits) for n in range(l)])\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "message = []\n",
    "for i in range(1,11):\n",
    "    message.append(generate_string(i*100))\n",
    "print(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encrypt_decrypt(message,elliptic_a,elliptic_b,d,p,q):\n",
    "    character_polynomials,N = koblitz_encoder(message,elliptic_a,elliptic_b) \n",
    "    public_key,private_key = generate_keypair(p,q,d,N)\n",
    "    cipher_polys = []\n",
    "    for element in character_polynomials:\n",
    "        cipher_text = encrypt(element,public_key,d,N,q)\n",
    "        cipher_polys.append(cipher_text)\n",
    "    dec_w = []\n",
    "    for element in cipher_polys:\n",
    "        decrypted_message = decrypt(element,private_key,p,q,N)\n",
    "        dec_w.append(decrypted_message.coeffs)\n",
    "    decrypted_plain_text = koblitz_decoder(points_decoder(dec_w))\n",
    "    if decrypted_plain_text == message:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3132212162017822\n",
      "0.4601268768310547\n",
      "0.744093656539917\n",
      "0.7337934970855713\n",
      "1.1180391311645508\n",
      "1.2552769184112549\n",
      "1.4147422313690186\n",
      "1.7700581550598145\n",
      "1.9001352787017822\n",
      "2.1019256114959717\n"
     ]
    }
   ],
   "source": [
    "d = 5\n",
    "p = 3\n",
    "q = 128\n",
    "time_taken = []\n",
    "for msg in message:\n",
    "    t1 = time.time()\n",
    "    result = encrypt_decrypt(msg,9,7,d,p,q)\n",
    "    t2 = time.time()\n",
    "    print(t2-t1)\n",
    "    time_taken.append(t2-t1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n"
     ]
    }
   ],
   "source": [
    "message_length = []\n",
    "for i in range(1,11):\n",
    "    message_length.append(i*100)\n",
    "print(message_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f316c49fa00>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure()\n",
    "plt.plot(message_length, time_taken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}