RSA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Haygen Tsoi\\AppData\\Local\\Temp\\ipykernel_7356\\359332957.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import rsa\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment on Key Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(size):\n",
    "    start = time.time()\n",
    "    (publickey, privatekey) = rsa.newkeys(size)\n",
    "    time_taken = (time.time() - start)\n",
    "    return time_taken\n",
    "\n",
    "key_gen_time = []\n",
    "size = []\n",
    "security = []\n",
    "schemes = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1024 bits (80 bits for Symmetric Key Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43949174880981445"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_taken = experiment(1024)\n",
    "key_gen_time.append(time_taken)\n",
    "size.append(1024)\n",
    "security.append(80)\n",
    "schemes.append(\"1024\")\n",
    "time_taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2048 bits (112 bits for Symmetric Key Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0309107303619385"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_taken = experiment(2048)\n",
    "key_gen_time.append(time_taken)\n",
    "size.append(2048)\n",
    "security.append(112)\n",
    "schemes.append(\"2048\")\n",
    "time_taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3072 bits (128 bits for Symmetric Key Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.89483904838562"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_taken = experiment(3072)\n",
    "key_gen_time.append(time_taken)\n",
    "size.append(3072)\n",
    "security.append(128)\n",
    "schemes.append(\"3072\")\n",
    "time_taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7680 bits (192 bits for Symmetric Key Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time_taken = experiment(7680)\n",
    "# key_gen_time.append(time_taken)\n",
    "key_gen_time.append(10000)\n",
    "size.append(7680)\n",
    "security.append(192)\n",
    "schemes.append(\"7680\")\n",
    "# time_taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15360 bits (256 bits for Symmetric Key Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time_taken = experiment(15360)\n",
    "# key_gen_time.append(time_taken)\n",
    "key_gen_time.append(10000)\n",
    "size.append(15360)\n",
    "security.append(256)\n",
    "schemes.append(\"15360\")\n",
    "# time_taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Haygen Tsoi\\AppData\\Local\\Temp\\ipykernel_7356\\4055388611.py:29: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  axes[1].annotate(txt, (df['Security(in bits)'][i], df['Key Size (in bits)'][i]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = pd.DataFrame({\n",
    "    'Time(in ms)': key_gen_time,\n",
    "    'Security(in bits)': security,\n",
    "    'Key Size (in bits)': size,\n",
    "}, index=schemes)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "threshold = 1000\n",
    "filtered_df = df[df['Time(in ms)'] < threshold]\n",
    "axes[0].scatter(filtered_df['Security(in bits)'], filtered_df['Time(in ms)'], color='blue')\n",
    "axes[0].set_title('Key Generation Time vs. Security')\n",
    "axes[0].set_xlabel('Security (in bits)')\n",
    "axes[0].set_ylabel('Time (in ms)')\n",
    "\n",
    "# Annotate the filtered points\n",
    "for i, txt in enumerate(filtered_df['Key Size (in bits)']):\n",
    "    axes[0].annotate(txt, (filtered_df['Security(in bits)'].iloc[i], filtered_df['Time(in ms)'].iloc[i]))\n",
    "\n",
    "# Fit and plot a trend line for the filtered data\n",
    "z = np.polyfit(filtered_df['Security(in bits)'], filtered_df['Time(in ms)'], 1)\n",
    "p = np.poly1d(z)\n",
    "axes[0].plot(filtered_df['Security(in bits)'], p(filtered_df['Security(in bits)']), \"b--\")\n",
    "\n",
    "# Plot 'Size(in bytes)' vs. 'Key Size(in bits)' for df\n",
    "axes[1].scatter(df['Security(in bits)'], df['Key Size (in bits)'], color='orange')\n",
    "# Annotate points from df\n",
    "for i, txt in enumerate(df.index):\n",
    "    axes[1].annotate(txt, (df['Security(in bits)'][i], df['Key Size (in bits)'][i]))\n",
    "\n",
    "z = np.polyfit(df['Security(in bits)'], df['Key Size (in bits)'], 1)\n",
    "p = np.poly1d(z)\n",
    "axes[1].plot(df['Security(in bits)'], p(df['Security(in bits)']), \"y--\")\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
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