notebooks/Syft - Paillier Homographic Encrypted Linear Classification example .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Paillier Homographic Encrypted Linear Classification example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a warm up code of what the possibilites are in the present library. It gives a snippet of the vision of the OpenMined project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from capsule.client import LocalCapsuleClient\n",
    "import syft as sy\n",
    "from sklearn.datasets import load_diabetes\n",
    "from syft.nn.linear import LinearClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\"\\x80\\x03cphe.paillier\\nPaillierPublicKey\\nq\\x00)\\x81q\\x01}q\\x02(X\\x01\\x00\\x00\\x00gq\\x03\\x8a\\x81h\\x91bJ\\xd1\\xbc\\xfe\\x11T\\xa1\\xbb\\x17\\xc0\\x06b\\xe7\\x1f\\xdd^\\xf1x\\xbb\\xf4\\xf2-\\xe2\\xe5\\x95|\\xe0\\x04F\\x133\\x9e\\x08\\xf4W\\xb6u<~\\xef\\xf3$\\x84Bz\\xd1\\xb9\\xbb\\xe3<\\xd7\\x12\\xe3\\x9c9\\\\\\x07*\\x13\\x13\\xd6-|L\\x01\\tS\\xc6\\xc4}\\xfb\\x87:\\x0c\\x02d\\xdabNX\\x8f~\\xe4\\xf05\\x04\\xe5\\x0e\\xc5^\\x8d\\xab\\xd1\\xa9Z\\xe3-u\\x13\\xda\\xa0\\xdd\\xd1*\\x89\\x986h\\xe8L\\xb9RY\\xddw\\xfep n\\xc5\\xf7OWb\\xad\\x00X\\x01\\x00\\x00\\x00nq\\x04\\x8a\\x81g\\x91bJ\\xd1\\xbc\\xfe\\x11T\\xa1\\xbb\\x17\\xc0\\x06b\\xe7\\x1f\\xdd^\\xf1x\\xbb\\xf4\\xf2-\\xe2\\xe5\\x95|\\xe0\\x04F\\x133\\x9e\\x08\\xf4W\\xb6u<~\\xef\\xf3$\\x84Bz\\xd1\\xb9\\xbb\\xe3<\\xd7\\x12\\xe3\\x9c9\\\\\\x07*\\x13\\x13\\xd6-|L\\x01\\tS\\xc6\\xc4}\\xfb\\x87:\\x0c\\x02d\\xdabNX\\x8f~\\xe4\\xf05\\x04\\xe5\\x0e\\xc5^\\x8d\\xab\\xd1\\xa9Z\\xe3-u\\x13\\xda\\xa0\\xdd\\xd1*\\x89\\x986h\\xe8L\\xb9RY\\xddw\\xfep n\\xc5\\xf7OWb\\xad\\x00X\\x07\\x00\\x00\\x00nsquareq\\x05\\x8b\\x00\\x01\\x00\\x00q\\xd7q11\\xd4\\x81\\x86\\xdb\\xbc\\x81\\xc0\\x18L\\x96\\x16\\x91\\xd6\\xb7M\\x14\\xc5\\xac.\\xf4Q\\x99+\\xbd\\xdeF\\x89Io/O\\xefUH\\x13\\x1c\\xbeRr\\x1d\\x86\\x91V\\tt\\xbeF-g`\\x97\\x0c\\xd9\\xaa\\x8f\\x18|N\\xe6/\\xc0\\xe0\\xf1m\\xfdg\\xdd\\x1d\\x8c\\xb0&\\xa90\\xcd\\x92:V\\x06\\xd4\\xc9\\xd0\\x8dNV\\xaf*\\x06U\\x0b)\\x8e dP\\x86|\\xba.\\xe9\\xb24\\xbd\\x91\\\\\\x0chL\\xbb\\xa9\\x86\\x139S\\xdd\\xbd\\x08\\x11P\\xb5\\xa2m\\x0f\\x99CgA\\x14YC)\\x8e\\xf8\\\\Cwz\\xc4<\\xbf'\\xff\\x9c\\x15\\x98\\xc2\\x17R\\xd9\\xd5\\x19-f\\xea\\xbaS\\\\%w\\xed;\\x06`\\x82\\x0f!q\\xf7#H\\xd9\\xae(\\x9dXY\\xbe4c\\x08f\\xb4Q\\xfb6[\\xc6?)\\xb99\\xa0\\xc6\\xc8\\x97\\xed\\xe1\\xf93\\xf8\\xb8\\xbe\\xde{x\\xbd\\xeb\\x96\\x88h\\xd4C\\x88\\xe9\\xa0\\xc7\\xd8\\xa8\\xec\\xf5&\\xb4~2C\\xf5\\xd0D9\\xa3\\xe2\\x01\\xd3]\\x88\\xe9Cw\\x11\\xeal\\x08\\x116c\\xfd\\xe1\\x0b\\xc9\\x0fnuX\\x07\\x00\\x00\\x00max_intq\\x06\\x8a\\x80v0v\\xc3E\\x94\\xff\\x05\\x1c\\x8b>]\\x95W\\xcb\\xf7_\\xf4tP(\\xe9\\xa6\\xfb\\xb9\\xa0L\\x87)\\xa0\\x01\\xc2[f\\xdf\\x02\\xfc\\xc7<'\\x14*\\xa5\\xa6a\\x81k\\xd3E\\x93>\\xa1i\\xf2\\xb0K4\\x13t\\x02\\x0e\\xb1[G\\x0f\\xd4n\\x00\\x03q\\x97A\\x7f\\xfe\\xd7hY\\xabv\\xf3 \\x1a\\xc8/*LPg\\x01\\xf7\\x04\\x97\\x1f/9\\x9b8\\x1e\\xa1d|\\x06\\x9e5\\x9f\\xf0\\xb8-\\x88g\\xcd\\xa2\\x19\\x93\\x1bs\\xf4'\\xaa%`\\xcfA\\xfdor\\xcb9ub.\"\n"
     ]
    }
   ],
   "source": [
    "cc = LocalCapsuleClient()\n",
    "pk = cc.keygen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearClassifier(n_inputs=10,n_labels=1).encrypt(pk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear Model (10,1): "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = load_diabetes()\n",
    "target = diabetes.target\n",
    "input = diabetes.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grads:PaillierTensor: [[e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]]\n",
      "Grads:PaillierTensor: [[e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]]\n",
      "Grads:PaillierTensor: [[e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]]\n",
      "Grads:PaillierTensor: [[e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]]\n",
      "Grads:PaillierTensor: [[e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]\n",
      " [e]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(\"Grads:\" + str((model.learn(input=input[i],target=target[i],alpha=0.5))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.decrypt(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "BaseTensor: [ 0.69049431]\n",
      "BaseTensor: [-0.42898593]\n",
      "BaseTensor: [-0.27561287]\n",
      "BaseTensor: [-1.29071817]\n",
      "BaseTensor: [-0.92240978]\n",
      "BaseTensor: [-0.89922203]\n",
      "BaseTensor: [-0.30918559]\n",
      "BaseTensor: [-0.39933307]\n",
      "BaseTensor: [-0.34693268]\n",
      "BaseTensor: [-0.75031461]\n",
      "BaseTensor: [-0.34422845]\n",
      "BaseTensor: [-0.36089593]\n",
      "BaseTensor: [-0.05148044]\n",
      "BaseTensor: [ 0.02524409]\n",
      "BaseTensor: [ 0.41160238]\n",
      "BaseTensor: [-0.64502009]\n",
      "BaseTensor: [ 0.16114465]\n",
      "BaseTensor: [ 0.70190699]\n",
      "BaseTensor: [ 0.20337816]\n",
      "BaseTensor: [-0.05104034]\n",
      "BaseTensor: [ 0.96797083]\n",
      "BaseTensor: [ 0.25194173]\n",
      "BaseTensor: [-0.36727426]\n",
      "BaseTensor: [ 0.02204251]\n",
      "BaseTensor: [ 0.8271318]\n",
      "BaseTensor: [-0.74908155]\n",
      "BaseTensor: [ 0.0218068]\n",
      "BaseTensor: [ 0.42133785]\n",
      "BaseTensor: [-0.22506713]\n",
      "BaseTensor: [-0.41693498]\n",
      "BaseTensor: [ 0.20860297]\n",
      "BaseTensor: [-0.26733557]\n",
      "BaseTensor: [-1.05240253]\n",
      "BaseTensor: [ 0.37062503]\n",
      "BaseTensor: [-0.36500677]\n",
      "BaseTensor: [ 1.92421595]\n",
      "BaseTensor: [-1.70932624]\n",
      "BaseTensor: [ 0.94464838]\n",
      "BaseTensor: [-0.07290435]\n",
      "BaseTensor: [-0.54238534]\n",
      "BaseTensor: [-1.47598523]\n",
      "BaseTensor: [ 0.07999902]\n",
      "BaseTensor: [ 0.52293977]\n",
      "BaseTensor: [ 0.82995281]\n",
      "BaseTensor: [ 0.45890556]\n",
      "BaseTensor: [-0.6960184]\n",
      "BaseTensor: [-0.22034338]\n",
      "BaseTensor: [-0.29267653]\n",
      "BaseTensor: [-0.06297612]\n",
      "BaseTensor: [ 0.45657423]\n",
      "BaseTensor: [-0.81161635]\n",
      "BaseTensor: [-1.07879455]\n",
      "BaseTensor: [-0.25833828]\n",
      "BaseTensor: [-0.1808161]\n",
      "BaseTensor: [-0.95680046]\n",
      "BaseTensor: [ 0.37785918]\n",
      "BaseTensor: [ 0.46873033]\n",
      "BaseTensor: [-0.21362241]\n",
      "BaseTensor: [-0.13266779]\n",
      "BaseTensor: [ 0.55373267]\n",
      "BaseTensor: [ 1.39888438]\n",
      "BaseTensor: [-0.96012571]\n",
      "BaseTensor: [-0.40504542]\n",
      "BaseTensor: [ 0.85568982]\n",
      "BaseTensor: [ 0.67294487]\n",
      "BaseTensor: [ 0.30776624]\n",
      "BaseTensor: [ 0.78346833]\n",
      "BaseTensor: [ 0.93699827]\n",
      "BaseTensor: [ 0.33185472]\n",
      "BaseTensor: [-0.08568221]\n",
      "BaseTensor: [-0.66517687]\n",
      "BaseTensor: [ 0.29936759]\n",
      "BaseTensor: [-0.26864428]\n",
      "BaseTensor: [ 1.63024897]\n",
      "BaseTensor: [ 0.29183232]\n",
      "BaseTensor: [ 0.80221359]\n",
      "BaseTensor: [-0.64930298]\n",
      "BaseTensor: [ 0.92708692]\n",
      "BaseTensor: [-1.09544331]\n",
      "BaseTensor: [ 0.3977323]\n",
      "BaseTensor: [ 0.40793563]\n",
      "BaseTensor: [ 0.73490802]\n",
      "BaseTensor: [ 0.60189612]\n",
      "BaseTensor: [ 0.27337574]\n",
      "BaseTensor: [ 0.60792608]\n",
      "BaseTensor: [-0.42804581]\n",
      "BaseTensor: [-0.18191332]\n",
      "BaseTensor: [-0.55602221]\n",
      "BaseTensor: [-0.1689342]\n",
      "BaseTensor: [ 0.03768911]\n",
      "BaseTensor: [ 0.3086323]\n",
      "BaseTensor: [ 1.46430684]\n",
      "BaseTensor: [ 1.62932338]\n",
      "BaseTensor: [ 0.29195441]\n",
      "BaseTensor: [-0.79226335]\n",
      "BaseTensor: [-0.31081317]\n",
      "BaseTensor: [ 0.12432121]\n",
      "BaseTensor: [ 0.93026945]\n",
      "BaseTensor: [-0.99548168]\n",
      "BaseTensor: [-0.46219546]\n",
      "BaseTensor: [-0.69861181]\n",
      "BaseTensor: [ 0.29443364]\n",
      "BaseTensor: [-0.2228965]\n",
      "BaseTensor: [ 0.42511747]\n",
      "BaseTensor: [-1.12224521]\n",
      "BaseTensor: [ 0.53245026]\n",
      "BaseTensor: [-0.39839497]\n",
      "BaseTensor: [-0.25458727]\n",
      "BaseTensor: [-0.6729502]\n",
      "BaseTensor: [ 0.48137219]\n",
      "BaseTensor: [ 0.11992176]\n",
      "BaseTensor: [ 1.25639549]\n",
      "BaseTensor: [-1.38745814]\n",
      "BaseTensor: [ 0.11499568]\n",
      "BaseTensor: [-1.5857445]\n",
      "BaseTensor: [-0.20764678]\n",
      "BaseTensor: [ 0.27516012]\n",
      "BaseTensor: [-0.15791325]\n",
      "BaseTensor: [-1.3157857]\n",
      "BaseTensor: [-0.91622524]\n",
      "BaseTensor: [-0.53126227]\n",
      "BaseTensor: [ 0.73871972]\n",
      "BaseTensor: [ 0.14669851]\n",
      "BaseTensor: [-0.08073748]\n",
      "BaseTensor: [-0.66734655]\n",
      "BaseTensor: [ 0.0520421]\n",
      "BaseTensor: [ 0.51215727]\n",
      "BaseTensor: [-1.52147642]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(input)):\n",
    "    print(model.forward(input[i]))"
   ]
  }
 ],
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