Experiments_DB_DE.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "c0081a55",
   "metadata": {},
   "source": [
    "## References\n",
    "- [1] Erlingsson, Pihur, and Korolova (2014) \"RAPPOR: Randomized aggregatable privacy-preserving ordinal response\" (ACM CCS).\n",
    "- [2] Arcolezi et al (2022) \"Improving the Utility of Locally Differentially Private Protocols for Longitudinal and Multidimensional Frequency Estimates\" (Digital Communications and Networks).\n",
    "- [3] Ding et al (2017) \"Collecting telemetry data privately\" (NIPS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bc2f0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "frac = 0.25 # (reduced dataset) frac=1 for results in the paper\n",
    "nb_seed = 5 # nb_seed=20 for results in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "682c75eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "params = {'axes.titlesize':'20','xtick.labelsize':'20','ytick.labelsize':'20','font.size':'18', 'legend.fontsize':'medium', 'lines.linewidth':'2.5', 'font.weight':'normal','lines.markersize':'16', 'text.latex.preamble': r'\\usepackage{amsfonts}', 'lines.markerfacecolor':'none'}\n",
    "matplotlib.rcParams.update(params)\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"cm\"\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "from sys import maxsize\n",
    "import time\n",
    "import numpy as np\n",
    "import xxhash\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9630fe15",
   "metadata": {},
   "source": [
    "## Importing protocols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e10b1132",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seeding function to ensure reproducibility\n",
    "from LDP.protocols import setting_seed\n",
    "# Our protocols\n",
    "from LDP.protocols import LOLOHA_Client, LOLOHA_Aggregator\n",
    "# Competitors\n",
    "from LDP.protocols import RAPPOR_Client, RAPPOR_Aggregator # [1]\n",
    "from LDP.protocols import L_OSUE_Client, L_OSUE_Aggregator # [2]\n",
    "from LDP.protocols import L_GRR_Client, L_GRR_Aggregator # [2]\n",
    "from LDP.protocols import dBitFlipPM_Client, dBitFlipPM_Aggregator # [3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d2dee8",
   "metadata": {},
   "source": [
    "## Reading PWGTP_Delaware dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b31d9af9",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/PWGTP_Delaware.csv')\n",
    "df.fillna(-99999, inplace=True)\n",
    "df.columns = range(df.shape[1])   \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23137e61",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "806fa24b",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <td>55</td>\n",
       "      <td>187</td>\n",
       "      <td>168</td>\n",
       "      <td>191</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>159</td>\n",
       "      <td>96</td>\n",
       "      <td>173</td>\n",
       "      <td>95</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>87</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>25</td>\n",
       "      <td>143</td>\n",
       "      <td>95</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>86</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>38</td>\n",
       "      <td>45</td>\n",
       "      <td>148</td>\n",
       "      <td>149</td>\n",
       "      <td>141</td>\n",
       "      <td>138</td>\n",
       "      <td>130</td>\n",
       "      <td>230</td>\n",
       "      <td>161</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>211</td>\n",
       "      <td>142</td>\n",
       "      <td>242</td>\n",
       "      <td>116</td>\n",
       "      <td>39</td>\n",
       "      <td>172</td>\n",
       "      <td>38</td>\n",
       "      <td>237</td>\n",
       "      <td>136</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>209</td>\n",
       "      <td>276</td>\n",
       "      <td>290</td>\n",
       "      <td>188</td>\n",
       "      <td>60</td>\n",
       "      <td>49</td>\n",
       "      <td>232</td>\n",
       "      <td>290</td>\n",
       "      <td>60</td>\n",
       "      <td>332</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>186</td>\n",
       "      <td>183</td>\n",
       "      <td>156</td>\n",
       "      <td>184</td>\n",
       "      <td>142</td>\n",
       "      <td>313</td>\n",
       "      <td>73</td>\n",
       "      <td>220</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>54</td>\n",
       "      <td>180</td>\n",
       "      <td>59</td>\n",
       "      <td>160</td>\n",
       "      <td>191</td>\n",
       "      <td>60</td>\n",
       "      <td>226</td>\n",
       "      <td>41</td>\n",
       "      <td>325</td>\n",
       "      <td>259</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>132</td>\n",
       "      <td>201</td>\n",
       "      <td>57</td>\n",
       "      <td>164</td>\n",
       "      <td>276</td>\n",
       "      <td>158</td>\n",
       "      <td>319</td>\n",
       "      <td>249</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>318</td>\n",
       "      <td>292</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>345</td>\n",
       "      <td>200</td>\n",
       "      <td>57</td>\n",
       "      <td>318</td>\n",
       "      <td>381</td>\n",
       "      <td>197</td>\n",
       "      <td>...</td>\n",
       "      <td>176</td>\n",
       "      <td>298</td>\n",
       "      <td>189</td>\n",
       "      <td>212</td>\n",
       "      <td>214</td>\n",
       "      <td>190</td>\n",
       "      <td>339</td>\n",
       "      <td>195</td>\n",
       "      <td>168</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2281 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   ...   70   71   72  \\\n",
       "0       0    0   25   46   23    0   27   23    0   34  ...    0    0    0   \n",
       "1      75  134  122   75   18  119   22   86   74   81  ...   21   21  140   \n",
       "2      36   32   41   10   30   66   34   59   77   11  ...   28   69   38   \n",
       "3     338   54  294  212   52   51   55  195  303   56  ...  183  187  174   \n",
       "4     168  159   96  173   95   94   29   87   27   81  ...  101   25  143   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2276   16   19   33   16   17    0    0   31   31    0  ...   17    0   30   \n",
       "2277   38   45  148  149  141  138  130  230  161   42  ...  211  142  242   \n",
       "2278  209  276  290  188   60   49  232  290   60  332  ...  173  186  183   \n",
       "2279   54  180   59  160  191   60  226   41  325  259  ...  172  132  201   \n",
       "2280  318  292   53   58  345  200   57  318  381  197  ...  176  298  189   \n",
       "\n",
       "       73   74   75   76   77   78   79  \n",
       "0      22   25   24   27   29   53   44  \n",
       "1      75   73   26  140   71   69  138  \n",
       "2      26   60   40   63   11   10   34  \n",
       "3     184   55   55  187  168  191  168  \n",
       "4      95   23   27   92   92   86  101  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  \n",
       "2276   31   18   17    2   19   19   16  \n",
       "2277  116   39  172   38  237  136  216  \n",
       "2278  156  184  142  313   73  220   55  \n",
       "2279   57  164  276  158  319  249  154  \n",
       "2280  212  214  190  339  195  168  187  \n",
       "\n",
       "[2281 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LE = LabelEncoder()\n",
    "LE.fit(np.array([val for col in df.columns for val in df[col]])) # considering all unique values\n",
    "\n",
    "for col in df.columns:\n",
    "    df[col] = LE.transform(df[col])\n",
    "\n",
    "df = df.sample(frac=frac) # to decrease execution time, we'll use frac% of the dataset\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4246ca21",
   "metadata": {},
   "source": [
    "## Static Parameteres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48e8a5f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Users = 2281\n",
      "Number of data collections =  80\n",
      "\n",
      "Attribute's domain size = 1234\n",
      "\n",
      "dBitFlipPM parameters\n",
      "Number of bukects b: 308\n",
      "Bucket size: 4.0064935064935066\n",
      "\n",
      "Privacy guarantees:\n",
      "List of epsilon_perm = [0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5. ]\n",
      "alpha = 0.4\n"
     ]
    }
   ],
   "source": [
    "# number of users (n)\n",
    "n = df.shape[0]\n",
    "print('Number of Users =',n)\n",
    "\n",
    "# number of data collections (tau)\n",
    "tau = df.shape[1]\n",
    "print('Number of data collections = ', tau)\n",
    "\n",
    "# attribute's domain size\n",
    "k = len(LE.classes_)\n",
    "print(\"\\nAttribute's domain size =\", k)\n",
    "\n",
    "print('\\ndBitFlipPM parameters')\n",
    "b = k // 4 # Number of buckets < k\n",
    "print(\"Number of bukects b:\", b)\n",
    "\n",
    "bulk_size = k / b\n",
    "print(\"Bucket size:\", bulk_size)\n",
    "\n",
    "print(\"\\nPrivacy guarantees:\")\n",
    "\n",
    "# upper bound (infinity reports)\n",
    "lst_eps_perm = np.arange(0.5, 5.1, 0.5)\n",
    "print(\"List of epsilon_perm =\", lst_eps_perm)\n",
    "\n",
    "# alpha value\n",
    "alpha = 0.4\n",
    "print(\"alpha =\", alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709d61cd",
   "metadata": {},
   "source": [
    "## Real frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f40b5087",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 30: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 31: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 32: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 33: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 34: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 35: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 36: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 37: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 38: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 39: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 40: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 41: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 42: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 43: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 44: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 45: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 46: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 47: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 48: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 49: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 50: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 51: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 52: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 53: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 54: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 55: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 56: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 57: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
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       " 58: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 59: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 60: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 61: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 62: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 63: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 64: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 65: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 66: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 67: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 68: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 69: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 70: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 71: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 72: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 73: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 74: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 75: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 76: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 77: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 78: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 79: array([0.00701447, 0.0004384 , 0.00219202, ..., 0.        , 0.        ,\n",
       "        0.        ])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_real = {t:0 for t in range(tau)}\n",
    "for t in range(tau):\n",
    "    real_freq = np.zeros(k)\n",
    "    for val in df[col].values:\n",
    "        real_freq[val]+=1\n",
    "    real_freq = real_freq / sum(real_freq)\n",
    "    dic_real[t] = real_freq\n",
    "dic_real"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cfec53",
   "metadata": {},
   "source": [
    "## Take average results due to randomness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ded5a6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save MSE per seed\n",
    "dic_mse = {seed: \n",
    "               {\"OLOLOHA\": [],\n",
    "                \"BiLOLOHA\": [],\n",
    "                \"L_OSUE\": [],\n",
    "                \"RAPPOR\": [],\n",
    "                \"L_GRR\": [],\n",
    "                \"1BitFlipPM\": [],\n",
    "                \"bBitFlipPM\": []\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }\n",
    "# Save budget consumption per seed\n",
    "dic_bdg = {seed: \n",
    "               {\"OLOLOHA\": -1,\n",
    "                \"BiLOLOHA\": -1,\n",
    "                \"L_OSUE\": -1,\n",
    "                \"RAPPOR\": -1,\n",
    "                \"L_GRR\": -1,\n",
    "                \"1BitFlipPM\": -1,\n",
    "                \"bBitFlipPM\": -1\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }\n",
    "\n",
    "# Save data change detection per seed\n",
    "dic_chg = {seed: \n",
    "               {\n",
    "                \"1BitFlipPM\": -1,\n",
    "                \"bBitFlipPM\": -1\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d6178e",
   "metadata": {},
   "source": [
    "## OLOLOLHA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "322719b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 43919.7377884388 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = LOLOHA_Client(row.values, k, eps_perm, eps_1, alpha, optimal=True)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = LOLOHA_Aggregator(reports[:, t], k, eps_perm, eps_1, alpha, optimal=True)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "    \n",
    "    dic_mse[seed][\"OLOLOHA\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"OLOLOHA\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c12702",
   "metadata": {},
   "source": [
    "## BiLOLOHA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e774569e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 20108.18863081932 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = LOLOHA_Client(row.values, k, eps_perm, eps_1, alpha, optimal=False)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = LOLOHA_Aggregator(reports[:, t], k, eps_perm, eps_1, alpha, optimal=False)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"BiLOLOHA\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"BiLOLOHA\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1dc3dd",
   "metadata": {},
   "source": [
    "## L-OSUE [2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2628f17d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 607.6110534667969 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = L_OSUE_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = L_OSUE_Aggregator(reports[:, t], eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"L_OSUE\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"L_OSUE\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2c7fe1",
   "metadata": {},
   "source": [
    "## RAPPOR [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2ffe554",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 637.9144685268402 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = RAPPOR_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = RAPPOR_Aggregator(reports[:, t], eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"RAPPOR\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"RAPPOR\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a74382",
   "metadata": {},
   "source": [
    "## L-GRR [2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a25202a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 117.76744341850281 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = L_GRR_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = L_GRR_Aggregator(reports[:, t], k, eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"L_GRR\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"L_GRR\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db482602",
   "metadata": {},
   "source": [
    "## 1BitFlipPM [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49c27858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 2736.3570063114166 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "# Number of bits each user sample/report\n",
    "d = 1\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    lst_detect_change = [] # List of 100% detected change\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        change_tracking = [] # List of Boolean value indicating data change detection for all values\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget, detect_change = dBitFlipPM_Client(row.values, k, b, d, eps_perm)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "            change_tracking.append(detect_change)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = np.zeros(b)\n",
    "            for val in df[t].values:\n",
    "                real_freq[int(val / bulk_size)]+=1\n",
    "            real_freq = real_freq/sum(real_freq)\n",
    "            \n",
    "            est_freq = dBitFlipPM_Aggregator(reports[:, t], b, d, eps_perm)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        lst_detect_change.append(100 * sum(change_tracking) / n) # Averaged data change detection in %\n",
    "        \n",
    "    dic_mse[seed][\"1BitFlipPM\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"1BitFlipPM\"] = lst_bdg_avg\n",
    "    dic_chg[seed][\"1BitFlipPM\"] = lst_detect_change\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c08141",
   "metadata": {},
   "source": [
    "## bBitFlipPM [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2fbf5e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 46153.912115335464 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "# Number of bits each user sample/report\n",
    "d = b\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    lst_detect_change = [] # List of 100% detected change\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        change_tracking = [] # List of Boolean value indicating data change detection for all values\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget, detect_change = dBitFlipPM_Client(row.values, k, b, d, eps_perm)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "            change_tracking.append(detect_change)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = np.zeros(b)\n",
    "            for val in df[t].values:\n",
    "                real_freq[int(val / bulk_size)]+=1\n",
    "            real_freq = real_freq/sum(real_freq)\n",
    "            \n",
    "            est_freq = dBitFlipPM_Aggregator(reports[:, t], b, d, eps_perm)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        lst_detect_change.append(100 * sum(change_tracking) / n) # Averaged data change detection in %\n",
    "        \n",
    "    dic_mse[seed][\"bBitFlipPM\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"bBitFlipPM\"] = lst_bdg_avg\n",
    "    dic_chg[seed][\"bBitFlipPM\"] = lst_detect_change\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7e5b32",
   "metadata": {},
   "source": [
    "## Plotting MSE_avg metric results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "822aa3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "# plt.plot(np.mean([dic_mse[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0), label='bBitFlipPM', marker='*')\n",
    "plt.plot(np.mean([dic_mse[seed][\"L_OSUE\"] for seed in range(nb_seed)], axis=0), label='L-OSUE',marker='s')\n",
    "plt.plot(np.mean([dic_mse[seed][\"OLOLOHA\"] for seed in range(nb_seed)], axis=0), label='OLOLOHA', marker='o')\n",
    "plt.plot(np.mean([dic_mse[seed][\"RAPPOR\"] for seed in range(nb_seed)], axis=0), label='RAPPOR',marker='X')\n",
    "plt.plot(np.mean([dic_mse[seed][\"BiLOLOHA\"] for seed in range(nb_seed)], axis=0), label='BiLOLOHA',marker='>')\n",
    "# plt.plot(np.mean([dic_mse[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0), label='1BitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_mse[seed][\"L_GRR\"] for seed in range(nb_seed)], axis=0), label='L-GRR', marker='D')\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('$MSE_{avg}$')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.title('$\\epsilon_1$ $=$' +f'${alpha}$'+ '$\\epsilon_{\\infty}$', loc='center')\n",
    "plt.legend(ncol=5, loc='upper center', bbox_to_anchor=(0.5, 1.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2033826",
   "metadata": {},
   "source": [
    "## Plotting privacy budget consumption metric results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "979f3515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "plt.plot(np.mean([dic_bdg[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='bBitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"L_OSUE\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='L-OSUE',marker='s')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"OLOLOHA\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='OLOLOHA', marker='o')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"RAPPOR\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='RAPPOR',marker='X')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"BiLOLOHA\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='BiLOLOHA',marker='>')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='1BitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"L_GRR\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='L-GRR', marker='D', color='tab:pink')\n",
    "\n",
    "\n",
    "ticks = np.arange(0,251,50)\n",
    "plt.yticks(ticks,ticks)\n",
    "# plt.set_yticklabels()\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('$\\check{\\epsilon}_{avg}$')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.title('$\\epsilon_1$ $=$' +f'${alpha}$'+ '$\\epsilon_{\\infty}$', loc='center')\n",
    "plt.legend(ncol=4, loc='upper center', bbox_to_anchor=(0.5, 1.35))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0e1ff4",
   "metadata": {},
   "source": [
    "## Plotting data change detection for dBitFlipPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e08b84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "plt.plot(np.mean([dic_chg[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0), label='bBitFlipPM', marker='*')\n",
    "plt.plot(np.mean([dic_chg[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0), label='1BitFlipPM', marker='P')\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('\\% of Users')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.legend(ncol=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cf88e6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}