102_UtilityEvaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "912308f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "310609b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epsilon</th>\n",
       "      <th>blockSize</th>\n",
       "      <th>maxErr</th>\n",
       "      <th>L1</th>\n",
       "      <th>KL</th>\n",
       "      <th>AverageProfileDistance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.401064</td>\n",
       "      <td>1.975598</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1792.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.398936</td>\n",
       "      <td>1.811273</td>\n",
       "      <td>1.278723</td>\n",
       "      <td>1693.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.398936</td>\n",
       "      <td>1.849129</td>\n",
       "      <td>1.280851</td>\n",
       "      <td>1675.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.916054</td>\n",
       "      <td>1.291489</td>\n",
       "      <td>1731.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.380851</td>\n",
       "      <td>1.386061</td>\n",
       "      <td>1.217021</td>\n",
       "      <td>1598.848485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Epsilon  blockSize    maxErr        L1        KL  AverageProfileDistance\n",
       "0      1.0          1  0.401064  1.975598  1.300000             1792.969697\n",
       "1      1.0          2  0.398936  1.811273  1.278723             1693.484848\n",
       "2      1.0          5  0.398936  1.849129  1.280851             1675.515152\n",
       "3      1.0         10  0.400000  1.916054  1.291489             1731.121212\n",
       "4      5.0          1  0.380851  1.386061  1.217021             1598.848485"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Priv = pd.read_csv('Utility_SanData.csv')\n",
    "Priv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f6ee40fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epsilon', ylabel='maxErr'>"
      ]
     },
     "execution_count": 37,
     "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": [
    "sns.barplot(x='Epsilon', y='maxErr', hue='blockSize', data=Priv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "da5a7196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epsilon', ylabel='L1'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sns.barplot(x='Epsilon', y='L1', hue='blockSize', data=Priv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "21f5aa73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Epsilon', ylabel='KL'>"
      ]
     },
     "execution_count": 39,
     "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": [
    "sns.barplot(x='Epsilon', y='KL', hue='blockSize', data=Priv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "63db9479",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Average distance in minutes for the activites\n",
    "ax = sns.barplot(x='Epsilon', y='AverageProfileDistance', hue='blockSize', data=Priv)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01923271",
   "metadata": {},
   "source": [
    "Some observations:\n",
    "1. For large privacy regimes (e.g., \\epsilon>5.0) the smaller block size we use the better usability we preserve. \n",
    "2. With this small dataset we need large privacy values to preserve usability (\\epsilon >=10). For example, for ep=10 and blockSize=5, we loose 200 mins of activity on averge per individual (over 30 days of recording)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7b5adf5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}