DataScience Tools - How to Anonymize Data.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## DataScience Tools \n",
    "#### How to Anonymize Data\n",
    "+ Scrubadub\n",
    "    + Anonymize data\n",
    "    + Remove personally identifiable information from free text\n",
    "    - pip install scrubadub \n",
    "+ NLP method eg Spacy NER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Pkgs\n",
    "import scrubadub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = \"Jessica has a friend in New York.Her email is jc@gmail.com\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Scrubber',\n",
       " 'VERSION',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'clean',\n",
       " 'detectors',\n",
       " 'exceptions',\n",
       " 'filth',\n",
       " 'import_magic',\n",
       " 'scrubbers',\n",
       " 'utils']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(scrubadub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{{NAME}} has a friend in {{NAME}} {{NAME}} email is {{EMAIL}}'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Redact/Anonymize Identifiers by default no consistency in Identifiers\n",
    "scrubadub.clean(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{{NAME-0}} has a friend in {{NAME-1}} {{NAME-2}} email is {{EMAIL-3}}'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace names with {{NAME-ID}} anonymous, but consistent IDs.\n",
    "scrubadub.clean(ex,replace_with='identifier')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppressing a particular detector from removing information.\n",
    "# To keep email or address etc\n",
    "scrubber = scrubadub.Scrubber()\n",
    "scrubber.remove_detector('email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{{NAME}} has a friend in {{NAME}} {{NAME}} email is jc@gmail.com'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleans Everything But Maintains Email\n",
    "scrubber.clean(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2\n",
    "+ Using Named Entity Recognition Procedure with Spacy or NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thank You\n",
    "# Jesse JCharis\n",
    "# Jesus Saves @JCharisTech\n",
    "# J-Secur1ty\n"
   ]
  }
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