mirror of
https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-05-05 01:32:38 +02:00
422 lines
12 KiB
Text
422 lines
12 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using ML anonymization to defend against membership inference attacks"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this tutorial we will show how to anonymize models using the ML anonymization module. \n",
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"\n",
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"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",
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"\n",
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"This will be demonstarted using the Adult dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/adult). \n",
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"\n",
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"For simplicity, we used only the numerical features in the dataset."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 39. 13. 2174. 0. 40.]\n",
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" [ 50. 13. 0. 0. 13.]\n",
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" [ 38. 9. 0. 0. 40.]\n",
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" ...\n",
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" [ 27. 13. 0. 0. 40.]\n",
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" [ 26. 11. 0. 0. 48.]\n",
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" [ 27. 9. 0. 0. 40.]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"# Use only numeric features (age, education-num, capital-gain, capital-loss, hours-per-week)\n",
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"x_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \")\n",
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"\n",
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"y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=14, dtype=str, delimiter=\", \")\n",
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"\n",
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"\n",
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"x_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \", skiprows=1)\n",
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"\n",
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"y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=14, dtype=str, delimiter=\", \", skiprows=1)\n",
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"\n",
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"# Trim trailing period \".\" from label\n",
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"y_test = np.array([a[:-1] for a in y_test])\n",
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"\n",
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"y_train[y_train == '<=50K'] = 0\n",
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"y_train[y_train == '>50K'] = 1\n",
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"y_train = y_train.astype(np.int)\n",
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"\n",
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"y_test[y_test == '<=50K'] = 0\n",
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"y_test[y_test == '>50K'] = 1\n",
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"y_test = y_test.astype(np.int)\n",
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"\n",
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"# get balanced dataset\n",
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"x_train = x_train[:x_test.shape[0]]\n",
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"y_train = y_train[:y_test.shape[0]]\n",
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"\n",
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"print(x_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train decision tree model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 116,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Base model accuracy: 0.8075056814691972\n"
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]
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}
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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier\n",
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"\n",
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"model = DecisionTreeClassifier()\n",
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"model.fit(x_train, y_train)\n",
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"\n",
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"art_classifier = ScikitlearnDecisionTreeClassifier(model)\n",
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"\n",
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"print('Base model accuracy: ', model.score(x_test, y_test))\n",
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"\n",
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"x_train_predictions = np.array([np.argmax(arr) for arr in art_classifier.predict(x_train)]).reshape(-1,1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Attack\n",
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"The black-box attack basically trains an additional classifier (called the attack model) to predict the membership status of a sample.\n",
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"#### Train attack model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 124,
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"metadata": {},
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"outputs": [],
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"source": [
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"from art.attacks.inference.membership_inference import MembershipInferenceBlackBox\n",
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"\n",
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"# attack_model_type can be nn (neural network), rf (randon forest) or gb (gradient boosting)\n",
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"bb_attack = MembershipInferenceBlackBox(art_classifier, attack_model_type='rf')\n",
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"\n",
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"# use half of each dataset for training the attack\n",
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"attack_train_ratio = 0.5\n",
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"attack_train_size = int(len(x_train) * attack_train_ratio)\n",
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"attack_test_size = int(len(x_test) * attack_train_ratio)\n",
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"\n",
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"# train attack model\n",
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"bb_attack.fit(x_train[:attack_train_size], y_train[:attack_train_size],\n",
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" x_test[:attack_test_size], y_test[:attack_test_size])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Infer sensitive feature and check accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 125,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.5440363591696352\n"
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]
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}
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],
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"source": [
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"# get inferred values for remaining half\n",
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"inferred_train_bb = bb_attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])\n",
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"inferred_test_bb = bb_attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])\n",
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"# check accuracy\n",
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"train_acc = np.sum(inferred_train_bb) / len(inferred_train_bb)\n",
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"test_acc = 1 - (np.sum(inferred_test_bb) / len(inferred_test_bb))\n",
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"acc = (train_acc * len(inferred_train_bb) + test_acc * len(inferred_test_bb)) / (len(inferred_train_bb) + len(inferred_test_bb))\n",
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"print(acc)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This means that for 54% of the data, membership is inferred correctly using this attack."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Anonymized data\n",
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"## k=100\n",
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"\n",
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"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",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 128,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[38. 13. 0. 0. 40.]\n",
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" [57. 13. 0. 0. 30.]\n",
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" [37. 9. 0. 0. 40.]\n",
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" ...\n",
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" [26. 13. 0. 0. 40.]\n",
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" [29. 10. 0. 0. 50.]\n",
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" [25. 9. 0. 0. 40.]]\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import sys\n",
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"sys.path.insert(0, os.path.abspath('..'))\n",
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"from apt.anonymization import Anonymize\n",
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"\n",
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"# QI = (age, education-num, capital-gain, hours-per-week)\n",
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"QI = [0, 1, 2, 4]\n",
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"anonymizer = Anonymize(100, QI)\n",
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"anon = anonymizer.anonymize(x_train, x_train_predictions)\n",
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"print(anon)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"6739"
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]
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},
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"execution_count": 104,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# number of distinct rows in original data\n",
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"len(np.unique(x_train, axis=0))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"658"
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]
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},
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"execution_count": 129,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# number of distinct rows in anonymized data\n",
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"len(np.unique(anon, axis=0))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train decision tree model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 130,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Anonymized model accuracy: 0.8304158221239482\n"
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]
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}
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],
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"source": [
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"anon_model = DecisionTreeClassifier()\n",
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"anon_model.fit(anon, y_train)\n",
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"\n",
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"anon_art_classifier = ScikitlearnDecisionTreeClassifier(anon_model)\n",
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"\n",
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"print('Anonymized model accuracy: ', anon_model.score(x_test, y_test))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Attack\n",
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"### Black-box attack"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 131,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.5034393809114359\n"
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]
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}
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],
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"source": [
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"anon_bb_attack = MembershipInferenceBlackBox(anon_art_classifier, attack_model_type='rf')\n",
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"\n",
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"# train attack model\n",
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"anon_bb_attack.fit(x_train[:attack_train_size], y_train[:attack_train_size],\n",
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" x_test[:attack_test_size], y_test[:attack_test_size])\n",
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"\n",
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"# get inferred values\n",
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"anon_inferred_train_bb = anon_bb_attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])\n",
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"anon_inferred_test_bb = anon_bb_attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])\n",
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"# check accuracy\n",
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"anon_train_acc = np.sum(anon_inferred_train_bb) / len(anon_inferred_train_bb)\n",
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"anon_test_acc = 1 - (np.sum(anon_inferred_test_bb) / len(anon_inferred_test_bb))\n",
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"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",
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"print(anon_acc)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Attack accuracy is reduced to 50% (eqiuvalent to random guessing)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 132,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(0.5298924372550654, 0.7806166318634075)\n",
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"(0.5030507735890172, 0.5671293452892765)\n"
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]
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}
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],
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"source": [
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"def calc_precision_recall(predicted, actual, positive_value=1):\n",
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" score = 0 # both predicted and actual are positive\n",
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" num_positive_predicted = 0 # predicted positive\n",
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" num_positive_actual = 0 # actual positive\n",
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" for i in range(len(predicted)):\n",
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" if predicted[i] == positive_value:\n",
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" num_positive_predicted += 1\n",
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" if actual[i] == positive_value:\n",
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" num_positive_actual += 1\n",
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" if predicted[i] == actual[i]:\n",
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" if predicted[i] == positive_value:\n",
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" score += 1\n",
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" \n",
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" if num_positive_predicted == 0:\n",
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" precision = 1\n",
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" else:\n",
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" precision = score / num_positive_predicted # the fraction of predicted “Yes” responses that are correct\n",
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" if num_positive_actual == 0:\n",
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" recall = 1\n",
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" else:\n",
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" recall = score / num_positive_actual # the fraction of “Yes” responses that are predicted correctly\n",
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"\n",
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" return precision, recall\n",
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"\n",
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"# regular\n",
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"print(calc_precision_recall(np.concatenate((inferred_train_bb, inferred_test_bb)), \n",
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" np.concatenate((np.ones(len(inferred_train_bb)), np.zeros(len(inferred_test_bb))))))\n",
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"# anon\n",
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"print(calc_precision_recall(np.concatenate((anon_inferred_train_bb, anon_inferred_test_bb)), \n",
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" np.concatenate((np.ones(len(anon_inferred_train_bb)), np.zeros(len(anon_inferred_test_bb))))))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Precision and recall are also reduced."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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