خطرات حریم خصوصی را با گزارش حریم خصوصی TensorFlow ارزیابی کنید

مشاهده در TensorFlow.org

در Google Colab اجرا شود

مشاهده منبع در GitHub

دانلود دفترچه یادداشت

بررسی اجمالی

در این نرم افزار کد یک مدل طبقه بندی تصویر ساده را بر روی مجموعه داده CIFAR10 آموزش می دهید و سپس از "حمله استنتاج عضویت" در برابر این مدل استفاده می کنید تا ارزیابی کنید که آیا مهاجم می تواند "حدس بزند" که آیا نمونه خاصی در مجموعه آموزشی وجود دارد یا خیر. . شما از گزارش حریم خصوصی TF برای تجسم نتایج چندین مدل و نقاط بازرسی مدل استفاده خواهید کرد.

برپایی

import numpy as np
from typing import Tuple
from scipy import special
from sklearn import metrics

import tensorflow as tf

import tensorflow_datasets as tfds

# Set verbosity.
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from sklearn.exceptions import ConvergenceWarning

import warnings
warnings.simplefilter(action="ignore", category=ConvergenceWarning)
warnings.simplefilter(action="ignore", category=FutureWarning)

TensorFlow Privacy را نصب کنید.

pip install tensorflow_privacy

from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResultsCollection
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyMetric
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyReportMetadata
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import privacy_report

import tensorflow_privacy

آموزش دو مدل، با معیارهای حریم خصوصی

در این بخش آموزش یک جفت از keras.Model طبقه بندی در CIFAR-10 مجموعه داده. در طول فرآیند آموزشی، معیارهای حریم خصوصی را جمع آوری می کند، که برای تولید گزارش در بخش بعدی استفاده می شود.

اولین قدم تعریف برخی از فراپارامترها است:

dataset = 'cifar10'
num_classes = 10
activation = 'relu'
num_conv = 3

batch_size=50
epochs_per_report = 2
total_epochs = 50

lr = 0.001

سپس مجموعه داده را بارگیری کنید. هیچ چیز خاصی برای حفظ حریم خصوصی در این کد وجود ندارد.

print('Loading the dataset.')
train_ds = tfds.as_numpy(
    tfds.load(dataset, split=tfds.Split.TRAIN, batch_size=-1))
test_ds = tfds.as_numpy(
    tfds.load(dataset, split=tfds.Split.TEST, batch_size=-1))
x_train = train_ds['image'].astype('float32') / 255.
y_train_indices = train_ds['label'][:, np.newaxis]
x_test = test_ds['image'].astype('float32') / 255.
y_test_indices = test_ds['label'][:, np.newaxis]

# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train_indices, num_classes)
y_test = tf.keras.utils.to_categorical(y_test_indices, num_classes)

input_shape = x_train.shape[1:]

assert x_train.shape[0] % batch_size == 0, "The tensorflow_privacy optimizer doesn't handle partial batches"

Loading the dataset.

سپس یک تابع برای ساخت مدل ها تعریف کنید.

def small_cnn(input_shape: Tuple[int],
              num_classes: int,
              num_conv: int,
              activation: str = 'relu') -> tf.keras.models.Sequential:
  """Setup a small CNN for image classification.

  Args:
    input_shape: Integer tuple for the shape of the images.
    num_classes: Number of prediction classes.
    num_conv: Number of convolutional layers.
    activation: The activation function to use for conv and dense layers.

  Returns:
    The Keras model.
  """
  model = tf.keras.models.Sequential()
  model.add(tf.keras.layers.Input(shape=input_shape))

  # Conv layers
  for _ in range(num_conv):
    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation=activation))
    model.add(tf.keras.layers.MaxPooling2D())

  model.add(tf.keras.layers.Flatten())
  model.add(tf.keras.layers.Dense(64, activation=activation))
  model.add(tf.keras.layers.Dense(num_classes))

  model.compile(
    loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
    optimizer=tf.keras.optimizers.Adam(learning_rate=lr),
    metrics=['accuracy'])

  return model

با استفاده از این تابع، دو مدل سه لایه CNN بسازید.

پیکربندی اول به استفاده از بهینه ساز SGD اساسی، دوم به استفاده از یک بهینه ساز متفاوت خصوصی ( tf_privacy.DPKerasAdamOptimizer )، بنابراین شما می توانید نتایج را مقایسه کنید.

model_2layers = small_cnn(
    input_shape, num_classes, num_conv=2, activation=activation)
model_3layers = small_cnn(
    input_shape, num_classes, num_conv=3, activation=activation)

برای جمع‌آوری معیارهای حریم خصوصی، یک تماس برگشتی تعریف کنید

بعد تعریف یک keras.callbacks.Callback به periorically اجرای برخی از حملات حریم خصوصی در برابر مدل و ورود به سیستم نتایج.

keras fit روش خواهد شد پاسخ on_epoch_end روش بعد از هر دوره آموزش. n استدلال (مبتنی بر 0) تعداد دوره است.

شما می توانید این روش را با نوشتن یک حلقه است که بارها و بارها خواستار اجرای Model.fit(..., epochs=epochs_per_report) و اجرا می شود کد حمله. در اینجا از callback استفاده می‌شود، زیرا تفکیک واضحی بین منطق آموزشی و منطق ارزیابی حریم خصوصی ایجاد می‌کند.

class PrivacyMetrics(tf.keras.callbacks.Callback):
  def __init__(self, epochs_per_report, model_name):
    self.epochs_per_report = epochs_per_report
    self.model_name = model_name
    self.attack_results = []

  def on_epoch_end(self, epoch, logs=None):
    epoch = epoch+1

    if epoch % self.epochs_per_report != 0:
      return

    print(f'\nRunning privacy report for epoch: {epoch}\n')

    logits_train = self.model.predict(x_train, batch_size=batch_size)
    logits_test = self.model.predict(x_test, batch_size=batch_size)

    prob_train = special.softmax(logits_train, axis=1)
    prob_test = special.softmax(logits_test, axis=1)

    # Add metadata to generate a privacy report.
    privacy_report_metadata = PrivacyReportMetadata(
        # Show the validation accuracy on the plot
        # It's what you send to train_accuracy that gets plotted.
        accuracy_train=logs['val_accuracy'], 
        accuracy_test=logs['val_accuracy'],
        epoch_num=epoch,
        model_variant_label=self.model_name)

    attack_results = mia.run_attacks(
        AttackInputData(
            labels_train=y_train_indices[:, 0],
            labels_test=y_test_indices[:, 0],
            probs_train=prob_train,
            probs_test=prob_test),
        SlicingSpec(entire_dataset=True, by_class=True),
        attack_types=(AttackType.THRESHOLD_ATTACK,
                      AttackType.LOGISTIC_REGRESSION),
        privacy_report_metadata=privacy_report_metadata)

    self.attack_results.append(attack_results)

مدل ها را آموزش دهید

بلوک کد بعدی دو مدل را آموزش می دهد. all_reports لیست استفاده شده است به جمع آوری تمام نتایج حاصل از تمام اجرا می شود آموزش مدل. گزارش فردی witht ها برچسب گذاری model_name ، بنابراین هیچ سردرگمی در مورد که مدل تولید شده است که گزارش وجود دارد.

all_reports = []

callback = PrivacyMetrics(epochs_per_report, "2 Layers")
history = model_2layers.fit(
      x_train,
      y_train,
      batch_size=batch_size,
      epochs=total_epochs,
      validation_data=(x_test, y_test),
      callbacks=[callback],
      shuffle=True)

all_reports.extend(callback.attack_results)

Epoch 1/50
1000/1000 [==============================] - 13s 4ms/step - loss: 1.5146 - accuracy: 0.4573 - val_loss: 1.2374 - val_accuracy: 0.5660
Epoch 2/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.1933 - accuracy: 0.5811 - val_loss: 1.1873 - val_accuracy: 0.5851

Running privacy report for epoch: 2

Epoch 3/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.0694 - accuracy: 0.6246 - val_loss: 1.0526 - val_accuracy: 0.6310
Epoch 4/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.9911 - accuracy: 0.6548 - val_loss: 0.9906 - val_accuracy: 0.6549

Running privacy report for epoch: 4

Epoch 5/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.9348 - accuracy: 0.6743 - val_loss: 0.9712 - val_accuracy: 0.6617
Epoch 6/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8881 - accuracy: 0.6912 - val_loss: 0.9595 - val_accuracy: 0.6671

Running privacy report for epoch: 6

Epoch 7/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7024 - val_loss: 0.9574 - val_accuracy: 0.6684
Epoch 8/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8147 - accuracy: 0.7161 - val_loss: 0.9397 - val_accuracy: 0.6740

Running privacy report for epoch: 8

Epoch 9/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7820 - accuracy: 0.7263 - val_loss: 0.9325 - val_accuracy: 0.6837
Epoch 10/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7533 - accuracy: 0.7362 - val_loss: 0.9431 - val_accuracy: 0.6843

Running privacy report for epoch: 10

Epoch 11/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7169 - accuracy: 0.7477 - val_loss: 0.9310 - val_accuracy: 0.6795
Epoch 12/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6892 - accuracy: 0.7569 - val_loss: 0.9043 - val_accuracy: 0.6975

Running privacy report for epoch: 12

Epoch 13/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6677 - accuracy: 0.7663 - val_loss: 0.9401 - val_accuracy: 0.6796
Epoch 14/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6401 - accuracy: 0.7741 - val_loss: 0.9464 - val_accuracy: 0.6880

Running privacy report for epoch: 14

Epoch 15/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6177 - accuracy: 0.7821 - val_loss: 0.9359 - val_accuracy: 0.6930
Epoch 16/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5978 - accuracy: 0.7913 - val_loss: 0.9634 - val_accuracy: 0.6896

Running privacy report for epoch: 16

Epoch 17/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5745 - accuracy: 0.7973 - val_loss: 0.9941 - val_accuracy: 0.6932
Epoch 18/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5553 - accuracy: 0.8036 - val_loss: 0.9790 - val_accuracy: 0.6974

Running privacy report for epoch: 18

Epoch 19/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5376 - accuracy: 0.8103 - val_loss: 0.9989 - val_accuracy: 0.6907
Epoch 20/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5152 - accuracy: 0.8192 - val_loss: 1.0245 - val_accuracy: 0.6878

Running privacy report for epoch: 20

Epoch 21/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5048 - accuracy: 0.8208 - val_loss: 1.0223 - val_accuracy: 0.6852
Epoch 22/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4847 - accuracy: 0.8284 - val_loss: 1.0498 - val_accuracy: 0.6866

Running privacy report for epoch: 22

Epoch 23/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4722 - accuracy: 0.8325 - val_loss: 1.0610 - val_accuracy: 0.6899
Epoch 24/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4562 - accuracy: 0.8387 - val_loss: 1.0973 - val_accuracy: 0.6771

Running privacy report for epoch: 24

Epoch 25/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4392 - accuracy: 0.8447 - val_loss: 1.1141 - val_accuracy: 0.6865
Epoch 26/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4269 - accuracy: 0.8485 - val_loss: 1.1928 - val_accuracy: 0.6771

Running privacy report for epoch: 26

Epoch 27/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4135 - accuracy: 0.8533 - val_loss: 1.1945 - val_accuracy: 0.6758
Epoch 28/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.4053 - accuracy: 0.8569 - val_loss: 1.2244 - val_accuracy: 0.6716

Running privacy report for epoch: 28

Epoch 29/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3880 - accuracy: 0.8611 - val_loss: 1.2362 - val_accuracy: 0.6789
Epoch 30/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3805 - accuracy: 0.8630 - val_loss: 1.2815 - val_accuracy: 0.6805

Running privacy report for epoch: 30

Epoch 31/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3756 - accuracy: 0.8656 - val_loss: 1.2973 - val_accuracy: 0.6762
Epoch 32/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3565 - accuracy: 0.8719 - val_loss: 1.3022 - val_accuracy: 0.6810

Running privacy report for epoch: 32

Epoch 33/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3494 - accuracy: 0.8749 - val_loss: 1.3248 - val_accuracy: 0.6756
Epoch 34/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3371 - accuracy: 0.8790 - val_loss: 1.3941 - val_accuracy: 0.6806

Running privacy report for epoch: 34

Epoch 35/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3248 - accuracy: 0.8839 - val_loss: 1.4391 - val_accuracy: 0.6666
Epoch 36/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3233 - accuracy: 0.8833 - val_loss: 1.5060 - val_accuracy: 0.6692

Running privacy report for epoch: 36

Epoch 37/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3109 - accuracy: 0.8882 - val_loss: 1.4624 - val_accuracy: 0.6724
Epoch 38/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.3057 - accuracy: 0.8900 - val_loss: 1.5133 - val_accuracy: 0.6644

Running privacy report for epoch: 38

Epoch 39/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2929 - accuracy: 0.8949 - val_loss: 1.5465 - val_accuracy: 0.6618
Epoch 40/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2868 - accuracy: 0.8970 - val_loss: 1.5882 - val_accuracy: 0.6696

Running privacy report for epoch: 40

Epoch 41/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2778 - accuracy: 0.8982 - val_loss: 1.6317 - val_accuracy: 0.6713
Epoch 42/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2782 - accuracy: 0.8999 - val_loss: 1.6993 - val_accuracy: 0.6630

Running privacy report for epoch: 42

Epoch 43/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2675 - accuracy: 0.9039 - val_loss: 1.7294 - val_accuracy: 0.6645
Epoch 44/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2587 - accuracy: 0.9068 - val_loss: 1.7614 - val_accuracy: 0.6561

Running privacy report for epoch: 44

Epoch 45/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2528 - accuracy: 0.9076 - val_loss: 1.7835 - val_accuracy: 0.6564
Epoch 46/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2410 - accuracy: 0.9129 - val_loss: 1.8550 - val_accuracy: 0.6648

Running privacy report for epoch: 46

Epoch 47/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2409 - accuracy: 0.9106 - val_loss: 1.8705 - val_accuracy: 0.6572
Epoch 48/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2328 - accuracy: 0.9146 - val_loss: 1.9110 - val_accuracy: 0.6593

Running privacy report for epoch: 48

Epoch 49/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2299 - accuracy: 0.9164 - val_loss: 1.9468 - val_accuracy: 0.6634
Epoch 50/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.2250 - accuracy: 0.9178 - val_loss: 2.0154 - val_accuracy: 0.6610

Running privacy report for epoch: 50

callback = PrivacyMetrics(epochs_per_report, "3 Layers")
history = model_3layers.fit(
      x_train,
      y_train,
      batch_size=batch_size,
      epochs=total_epochs,
      validation_data=(x_test, y_test),
      callbacks=[callback],
      shuffle=True)

all_reports.extend(callback.attack_results)

Epoch 1/50
1000/1000 [==============================] - 4s 4ms/step - loss: 1.6838 - accuracy: 0.3772 - val_loss: 1.4805 - val_accuracy: 0.4552
Epoch 2/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.3938 - accuracy: 0.4969 - val_loss: 1.3291 - val_accuracy: 0.5276

Running privacy report for epoch: 2

Epoch 3/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.2564 - accuracy: 0.5510 - val_loss: 1.2313 - val_accuracy: 0.5627
Epoch 4/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.1610 - accuracy: 0.5884 - val_loss: 1.1251 - val_accuracy: 0.6039

Running privacy report for epoch: 4

Epoch 5/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.1034 - accuracy: 0.6105 - val_loss: 1.1168 - val_accuracy: 0.6063
Epoch 6/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.0476 - accuracy: 0.6319 - val_loss: 1.0716 - val_accuracy: 0.6248

Running privacy report for epoch: 6

Epoch 7/50
1000/1000 [==============================] - 3s 3ms/step - loss: 1.0107 - accuracy: 0.6461 - val_loss: 1.0264 - val_accuracy: 0.6407
Epoch 8/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.9731 - accuracy: 0.6597 - val_loss: 1.0216 - val_accuracy: 0.6447

Running privacy report for epoch: 8

Epoch 9/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.9437 - accuracy: 0.6712 - val_loss: 1.0016 - val_accuracy: 0.6467
Epoch 10/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.9191 - accuracy: 0.6790 - val_loss: 0.9845 - val_accuracy: 0.6553

Running privacy report for epoch: 10

Epoch 11/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8923 - accuracy: 0.6877 - val_loss: 0.9560 - val_accuracy: 0.6670
Epoch 12/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8722 - accuracy: 0.6959 - val_loss: 0.9518 - val_accuracy: 0.6686

Running privacy report for epoch: 12

Epoch 13/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7029 - val_loss: 0.9427 - val_accuracy: 0.6787
Epoch 14/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8305 - accuracy: 0.7116 - val_loss: 0.9247 - val_accuracy: 0.6814

Running privacy report for epoch: 14

Epoch 15/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.8164 - accuracy: 0.7157 - val_loss: 0.9263 - val_accuracy: 0.6797
Epoch 16/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7973 - accuracy: 0.7220 - val_loss: 0.9151 - val_accuracy: 0.6850

Running privacy report for epoch: 16

Epoch 17/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7830 - accuracy: 0.7277 - val_loss: 0.9139 - val_accuracy: 0.6842
Epoch 18/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7704 - accuracy: 0.7294 - val_loss: 0.9384 - val_accuracy: 0.6774

Running privacy report for epoch: 18

Epoch 19/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7539 - accuracy: 0.7366 - val_loss: 0.9508 - val_accuracy: 0.6761
Epoch 20/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7445 - accuracy: 0.7412 - val_loss: 0.9108 - val_accuracy: 0.6908

Running privacy report for epoch: 20

Epoch 21/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7343 - accuracy: 0.7418 - val_loss: 0.9161 - val_accuracy: 0.6855
Epoch 22/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7213 - accuracy: 0.7458 - val_loss: 0.9754 - val_accuracy: 0.6724

Running privacy report for epoch: 22

Epoch 23/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7133 - accuracy: 0.7487 - val_loss: 0.8936 - val_accuracy: 0.6984
Epoch 24/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.7072 - accuracy: 0.7504 - val_loss: 0.8872 - val_accuracy: 0.7002

Running privacy report for epoch: 24

Epoch 25/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6932 - accuracy: 0.7570 - val_loss: 0.9732 - val_accuracy: 0.6769
Epoch 26/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6883 - accuracy: 0.7578 - val_loss: 0.9332 - val_accuracy: 0.6798

Running privacy report for epoch: 26

Epoch 27/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6766 - accuracy: 0.7614 - val_loss: 0.9069 - val_accuracy: 0.6998
Epoch 28/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6656 - accuracy: 0.7662 - val_loss: 0.8879 - val_accuracy: 0.7011

Running privacy report for epoch: 28

Epoch 29/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6594 - accuracy: 0.7674 - val_loss: 0.8988 - val_accuracy: 0.7037
Epoch 30/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6499 - accuracy: 0.7700 - val_loss: 0.9086 - val_accuracy: 0.7001

Running privacy report for epoch: 30

Epoch 31/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6420 - accuracy: 0.7746 - val_loss: 0.8985 - val_accuracy: 0.7034
Epoch 32/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6354 - accuracy: 0.7742 - val_loss: 0.9089 - val_accuracy: 0.7018

Running privacy report for epoch: 32

Epoch 33/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6293 - accuracy: 0.7759 - val_loss: 0.9258 - val_accuracy: 0.6947
Epoch 34/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6192 - accuracy: 0.7851 - val_loss: 0.9326 - val_accuracy: 0.6976

Running privacy report for epoch: 34

Epoch 35/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6157 - accuracy: 0.7831 - val_loss: 0.9240 - val_accuracy: 0.6973
Epoch 36/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6063 - accuracy: 0.7853 - val_loss: 0.9504 - val_accuracy: 0.6971

Running privacy report for epoch: 36

Epoch 37/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.6036 - accuracy: 0.7867 - val_loss: 0.9025 - val_accuracy: 0.7094
Epoch 38/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5958 - accuracy: 0.7877 - val_loss: 0.9290 - val_accuracy: 0.6976

Running privacy report for epoch: 38

Epoch 39/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5900 - accuracy: 0.7919 - val_loss: 0.9379 - val_accuracy: 0.6963
Epoch 40/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5856 - accuracy: 0.7928 - val_loss: 0.9911 - val_accuracy: 0.6896

Running privacy report for epoch: 40

Epoch 41/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5772 - accuracy: 0.7944 - val_loss: 0.9093 - val_accuracy: 0.7059
Epoch 42/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5752 - accuracy: 0.7940 - val_loss: 0.9275 - val_accuracy: 0.7061

Running privacy report for epoch: 42

Epoch 43/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5645 - accuracy: 0.7998 - val_loss: 0.9208 - val_accuracy: 0.7025
Epoch 44/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5632 - accuracy: 0.8000 - val_loss: 0.9746 - val_accuracy: 0.6976

Running privacy report for epoch: 44

Epoch 45/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5557 - accuracy: 0.8045 - val_loss: 0.9211 - val_accuracy: 0.7098
Epoch 46/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5469 - accuracy: 0.8073 - val_loss: 0.9357 - val_accuracy: 0.7055

Running privacy report for epoch: 46

Epoch 47/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5438 - accuracy: 0.8062 - val_loss: 0.9495 - val_accuracy: 0.7025
Epoch 48/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5437 - accuracy: 0.8069 - val_loss: 0.9509 - val_accuracy: 0.6994

Running privacy report for epoch: 48

Epoch 49/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5414 - accuracy: 0.8066 - val_loss: 0.9780 - val_accuracy: 0.6939
Epoch 50/50
1000/1000 [==============================] - 3s 3ms/step - loss: 0.5321 - accuracy: 0.8108 - val_loss: 1.0109 - val_accuracy: 0.6846

Running privacy report for epoch: 50

توطئه های دوران

می‌توانید با بررسی دوره‌ای مدل (مثلاً هر 5 دوره)، هنگام آموزش مدل‌ها، خطرات حریم خصوصی را تجسم کنید که می‌توانید نقطه زمانی را با بهترین عملکرد/معادل حریم خصوصی انتخاب کنید.

استفاده از TF حریم ماژول عضویت استنتاج حمله به تولید AttackResults . این AttackResults از به یک ترکیب AttackResultsCollection . گزارش TF حریم طراحی شده است به تجزیه و تحلیل ارائه AttackResultsCollection .

results = AttackResultsCollection(all_reports)

privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
epoch_plot = privacy_report.plot_by_epochs(
    results, privacy_metrics=privacy_metrics)

ببینید که به عنوان یک قاعده، با افزایش تعداد دوره‌ها، آسیب‌پذیری حریم خصوصی بیشتر می‌شود. این در انواع مدل ها و همچنین انواع مختلف مهاجم صادق است.

مدل‌های دو لایه (با لایه‌های کانولوشنال کمتر) به طور کلی آسیب‌پذیرتر از مدل‌های سه لایه‌ی مشابه خود هستند.

حال بیایید ببینیم که عملکرد مدل با توجه به ریسک حریم خصوصی چگونه تغییر می کند.

حریم خصوصی در مقابل ابزار مفید

privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
utility_privacy_plot = privacy_report.plot_privacy_vs_accuracy(
    results, privacy_metrics=privacy_metrics)

for axis in utility_privacy_plot.axes:
  axis.set_xlabel('Validation accuracy')

مدل‌های سه لایه (شاید به دلیل پارامترهای زیاد) تنها به دقت قطار 0.85 دست می‌یابند. مدل‌های دو لایه عملکرد تقریباً برابری را برای آن سطح از خطر حفظ حریم خصوصی به دست می‌آورند، اما همچنان به دقت بهتری دست می‌یابند.

شما همچنین می توانید ببینید که چگونه خط برای مدل های دو لایه شیب دار تر می شود. این بدان معنی است که دستاوردهای حاشیه ای اضافی در دقت قطار به قیمت آسیب پذیری های گسترده حریم خصوصی است.

این پایان آموزش است. با خیال راحت نتایج خود را تجزیه و تحلیل کنید.