एमएल समुदाय दिवस 9 नवंबर है! TensorFlow, JAX से नई जानकारी के लिए हमसे जुड़ें, और अधिक जानें

TensorFlow गोपनीयता रिपोर्ट के साथ गोपनीयता जोखिमों का आकलन करें

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर स्रोत देखें नोटबुक डाउनलोड करें

अवलोकन

इस कोडलैब में आप CIFAR10 डेटासेट पर एक साधारण छवि वर्गीकरण मॉडल को प्रशिक्षित करेंगे, और फिर इस मॉडल के खिलाफ "सदस्यता अनुमान हमले" का उपयोग यह आकलन करने के लिए करेंगे कि क्या हमलावर "अनुमान लगाने" में सक्षम है कि क्या प्रशिक्षण सेट में कोई विशेष नमूना मौजूद था। . आप कई मॉडलों और मॉडल चौकियों से परिणामों की कल्पना करने के लिए TF गोपनीयता रिपोर्ट का उपयोग करेंगे।

सेट अप

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 गोपनीयता स्थापित करें।

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

गोपनीयता मेट्रिक्स के साथ दो मॉडलों को प्रशिक्षित करें

यह खंड की एक जोड़ी गाड़ियों keras.Model पर classifiers CIFAR-10 डाटासेट। प्रशिक्षण प्रक्रिया के दौरान यह गोपनीयता मेट्रिक्स एकत्र करता है, जिसका उपयोग अगले अनुभाग में रिपोर्ट तैयार करने के लिए किया जाएगा।

कुछ हाइपरपैरामीटर को परिभाषित करने के लिए पहला कदम है:

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

batch_size=50
epochs_per_report = 2
total_epochs = 50

lr = 0.001

इसके बाद, डेटासेट लोड करें। इस कोड में गोपनीयता-विशिष्ट कुछ भी नहीं है।

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.

अगला मॉडल बनाने के लिए एक फ़ंक्शन को परिभाषित करें।

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

उस फ़ंक्शन का उपयोग करके दो तीन-परत CNN मॉडल बनाएं।

एक भिन्न निजी अनुकूलक (उपयोग करने के लिए एक बुनियादी SGD अनुकूलक का उपयोग करने के लिए पहले, एक दूसरे को कॉन्फ़िगर tf_privacy.DPKerasAdamOptimizer ,) ताकि आप परिणामों की तुलना कर सकते हैं।

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)

गोपनीयता मेट्रिक्स एकत्र करने के लिए कॉलबैक को परिभाषित करें

अगला एक परिभाषित keras.callbacks.Callback periorically मॉडल के खिलाफ कुछ गोपनीयता हमलों को चलाने के लिए, और परिणाम लॉग इन करें।

Keras fit विधि कॉल करेंगे on_epoch_end प्रत्येक प्रशिक्षण युग के बाद विधि। n तर्क (0-आधारित) युग संख्या है।

आप एक पाश है कि बार-बार कॉल लिख कर इस प्रक्रिया को लागू कर सकता है Model.fit(..., epochs=epochs_per_report) और हमले कोड चलाता है। कॉलबैक का उपयोग यहां सिर्फ इसलिए किया जाता है क्योंकि यह प्रशिक्षण तर्क और गोपनीयता मूल्यांकन तर्क के बीच स्पष्ट अलगाव देता है।

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)

मॉडलों को प्रशिक्षित करें

अगला कोड ब्लॉक दो मॉडलों को प्रशिक्षित करता है। all_reports सूची सभी मॉडलों का प्रशिक्षण रन के सभी परिणामों को एकत्र करने के लिए प्रयोग किया जाता है। अलग-अलग रिपोर्ट witht टैग किए गए हैं model_name , इसलिए वहाँ कोई भ्रम के बारे में जो भी मॉडल जेनरेट जो रिपोर्ट है।

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

युग भूखंड

आप कल्पना कर सकते हैं कि गोपनीयता जोखिम कैसे होता है जब आप मॉडल की समय-समय पर जांच करके मॉडल को प्रशिक्षित करते हैं (उदाहरण के लिए प्रत्येक 5 युग), आप सर्वोत्तम प्रदर्शन / गोपनीयता व्यापार-बंद के साथ समय पर बिंदु चुन सकते हैं।

उत्पन्न करने के लिए TF गोपनीयता सदस्यता निष्कर्ष हमला मॉड्यूल का उपयोग करें AttackResults । ये AttackResults एक में संयुक्त हो AttackResultsCollection । TF गोपनीयता रिपोर्ट प्रदान की विश्लेषण करने के लिए डिज़ाइन किया गया है AttackResultsCollection

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

पीएनजी

देखें कि एक नियम के रूप में, जैसे-जैसे युगों की संख्या बढ़ती जाती है, गोपनीयता की भेद्यता बढ़ती जाती है। यह मॉडल वेरिएंट के साथ-साथ विभिन्न हमलावर प्रकारों के लिए भी सही है।

दो परत मॉडल (कम दृढ़ परतों के साथ) आम तौर पर उनके तीन परत मॉडल समकक्षों की तुलना में अधिक कमजोर होते हैं।

अब देखते हैं कि गोपनीयता जोखिम के संबंध में मॉडल का प्रदर्शन कैसे बदलता है।

गोपनीयता बनाम उपयोगिता

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')

पीएनजी

तीन परत मॉडल (शायद बहुत अधिक मापदंडों के कारण) केवल 0.85 की ट्रेन सटीकता प्राप्त करते हैं। दो परत मॉडल गोपनीयता जोखिम के उस स्तर के लिए लगभग समान प्रदर्शन प्राप्त करते हैं लेकिन वे बेहतर सटीकता प्राप्त करना जारी रखते हैं।

आप यह भी देख सकते हैं कि दो परत मॉडल के लिए लाइन कैसे तेज हो जाती है। इसका मतलब यह है कि ट्रेन की सटीकता में अतिरिक्त मामूली लाभ विशाल गोपनीयता कमजोरियों की कीमत पर आता है।

यह ट्यूटोरियल का अंत है। अपने स्वयं के परिणामों का विश्लेषण करने के लिए स्वतंत्र महसूस करें।