{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using ML anonymization to defend against membership inference attacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will show how to anonymize models using the ML anonymization module. \n",
    "\n",
    "We will demonstrate running inference attacks both on a vanilla model, and then on an anonymized version of the model. We will run a black-box membership inference attack using ART's inference module (https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/art/attacks/inference). \n",
    "\n",
    "This will be demonstarted using the Adult dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/adult). \n",
    "\n",
    "For simplicity, we used only the numerical features in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  39.   13. 2174.    0.   40.]\n",
      " [  50.   13.    0.    0.   13.]\n",
      " [  38.    9.    0.    0.   40.]\n",
      " ...\n",
      " [  27.   13.    0.    0.   40.]\n",
      " [  26.   11.    0.    0.   48.]\n",
      " [  27.    9.    0.    0.   40.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Use only numeric features (age, education-num, capital-gain, capital-loss, hours-per-week)\n",
    "x_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
    "                        usecols=(0, 4, 10, 11, 12), delimiter=\", \")\n",
    "\n",
    "y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
    "                        usecols=14, dtype=str, delimiter=\", \")\n",
    "\n",
    "\n",
    "x_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
    "                        usecols=(0, 4, 10, 11, 12), delimiter=\", \", skiprows=1)\n",
    "\n",
    "y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
    "                        usecols=14, dtype=str, delimiter=\", \", skiprows=1)\n",
    "\n",
    "# Trim trailing period \".\" from label\n",
    "y_test = np.array([a[:-1] for a in y_test])\n",
    "\n",
    "y_train[y_train == '<=50K'] = 0\n",
    "y_train[y_train == '>50K'] = 1\n",
    "y_train = y_train.astype(np.int)\n",
    "\n",
    "y_test[y_test == '<=50K'] = 0\n",
    "y_test[y_test == '>50K'] = 1\n",
    "y_test = y_test.astype(np.int)\n",
    "\n",
    "# get balanced dataset\n",
    "x_train = x_train[:x_test.shape[0]]\n",
    "y_train = y_train[:y_test.shape[0]]\n",
    "\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base model accuracy:  0.8075056814691972\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "art_classifier = ScikitlearnDecisionTreeClassifier(model)\n",
    "\n",
    "print('Base model accuracy: ', model.score(x_test, y_test))\n",
    "\n",
    "x_train_predictions = np.array([np.argmax(arr) for arr in art_classifier.predict(x_train)]).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attack\n",
    "The black-box attack basically trains an additional classifier (called the attack model) to predict the membership status of a sample.\n",
    "#### Train attack model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from art.attacks.inference.membership_inference import MembershipInferenceBlackBox\n",
    "\n",
    "# attack_model_type can be nn (neural network), rf (randon forest) or gb (gradient boosting)\n",
    "bb_attack = MembershipInferenceBlackBox(art_classifier, attack_model_type='rf')\n",
    "\n",
    "# use half of each dataset for training the attack\n",
    "attack_train_ratio = 0.5\n",
    "attack_train_size = int(len(x_train) * attack_train_ratio)\n",
    "attack_test_size = int(len(x_test) * attack_train_ratio)\n",
    "\n",
    "# train attack model\n",
    "bb_attack.fit(x_train[:attack_train_size], y_train[:attack_train_size],\n",
    "              x_test[:attack_test_size], y_test[:attack_test_size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Infer sensitive feature and check accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5440363591696352\n"
     ]
    }
   ],
   "source": [
    "# get inferred values for remaining half\n",
    "inferred_train_bb = bb_attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])\n",
    "inferred_test_bb = bb_attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])\n",
    "# check accuracy\n",
    "train_acc = np.sum(inferred_train_bb) / len(inferred_train_bb)\n",
    "test_acc = 1 - (np.sum(inferred_test_bb) / len(inferred_test_bb))\n",
    "acc = (train_acc * len(inferred_train_bb) + test_acc * len(inferred_test_bb)) / (len(inferred_train_bb) + len(inferred_test_bb))\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that for 54% of the data, membership is inferred correctly using this attack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anonymized data\n",
    "## k=100\n",
    "\n",
    "Now we will apply the same attacks on an anonymized version of the same dataset (k=100). The data is anonymized on the quasi-identifiers: age, education-num, capital-gain, hours-per-week.\n",
    "\n",
    "k=100 means that each record in the anonymized dataset is identical to 99 others on the quasi-identifier values (i.e., when looking only at those features, the records are indistinguishable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[38. 13.  0.  0. 40.]\n",
      " [57. 13.  0.  0. 30.]\n",
      " [37.  9.  0.  0. 40.]\n",
      " ...\n",
      " [26. 13.  0.  0. 40.]\n",
      " [29. 10.  0.  0. 50.]\n",
      " [25.  9.  0.  0. 40.]]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "from apt.anonymization import Anonymize\n",
    "\n",
    "# QI = (age, education-num, capital-gain, hours-per-week)\n",
    "QI = [0, 1, 2, 4]\n",
    "anonymizer = Anonymize(100, QI)\n",
    "anon = anonymizer.anonymize(x_train, x_train_predictions)\n",
    "print(anon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6739"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of distinct rows in original data\n",
    "len(np.unique(x_train, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "658"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of distinct rows in anonymized data\n",
    "len(np.unique(anon, axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anonymized model accuracy:  0.8304158221239482\n"
     ]
    }
   ],
   "source": [
    "anon_model = DecisionTreeClassifier()\n",
    "anon_model.fit(anon, y_train)\n",
    "\n",
    "anon_art_classifier = ScikitlearnDecisionTreeClassifier(anon_model)\n",
    "\n",
    "print('Anonymized model accuracy: ', anon_model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attack\n",
    "### Black-box attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5034393809114359\n"
     ]
    }
   ],
   "source": [
    "anon_bb_attack = MembershipInferenceBlackBox(anon_art_classifier, attack_model_type='rf')\n",
    "\n",
    "# train attack model\n",
    "anon_bb_attack.fit(x_train[:attack_train_size], y_train[:attack_train_size],\n",
    "                   x_test[:attack_test_size], y_test[:attack_test_size])\n",
    "\n",
    "# get inferred values\n",
    "anon_inferred_train_bb = anon_bb_attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])\n",
    "anon_inferred_test_bb = anon_bb_attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])\n",
    "# check accuracy\n",
    "anon_train_acc = np.sum(anon_inferred_train_bb) / len(anon_inferred_train_bb)\n",
    "anon_test_acc = 1 - (np.sum(anon_inferred_test_bb) / len(anon_inferred_test_bb))\n",
    "anon_acc = (anon_train_acc * len(anon_inferred_train_bb) + anon_test_acc * len(anon_inferred_test_bb)) / (len(anon_inferred_train_bb) + len(anon_inferred_test_bb))\n",
    "print(anon_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attack accuracy is reduced to 50% (eqiuvalent to random guessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.5298924372550654, 0.7806166318634075)\n",
      "(0.5030507735890172, 0.5671293452892765)\n"
     ]
    }
   ],
   "source": [
    "def calc_precision_recall(predicted, actual, positive_value=1):\n",
    "    score = 0  # both predicted and actual are positive\n",
    "    num_positive_predicted = 0  # predicted positive\n",
    "    num_positive_actual = 0  # actual positive\n",
    "    for i in range(len(predicted)):\n",
    "        if predicted[i] == positive_value:\n",
    "            num_positive_predicted += 1\n",
    "        if actual[i] == positive_value:\n",
    "            num_positive_actual += 1\n",
    "        if predicted[i] == actual[i]:\n",
    "            if predicted[i] == positive_value:\n",
    "                score += 1\n",
    "    \n",
    "    if num_positive_predicted == 0:\n",
    "        precision = 1\n",
    "    else:\n",
    "        precision = score / num_positive_predicted  # the fraction of predicted “Yes” responses that are correct\n",
    "    if num_positive_actual == 0:\n",
    "        recall = 1\n",
    "    else:\n",
    "        recall = score / num_positive_actual  # the fraction of “Yes” responses that are predicted correctly\n",
    "\n",
    "    return precision, recall\n",
    "\n",
    "# regular\n",
    "print(calc_precision_recall(np.concatenate((inferred_train_bb, inferred_test_bb)), \n",
    "                            np.concatenate((np.ones(len(inferred_train_bb)), np.zeros(len(inferred_test_bb))))))\n",
    "# anon\n",
    "print(calc_precision_recall(np.concatenate((anon_inferred_train_bb, anon_inferred_test_bb)), \n",
    "                            np.concatenate((np.ones(len(anon_inferred_train_bb)), np.zeros(len(anon_inferred_test_bb))))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision and recall are also reduced."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}