העריכו את סיכוני הפרטיות בעזרת דוח הפרטיות של TensorFlow

קל לארגן דפים בעזרת אוספים אפשר לשמור ולסווג תוכן על סמך ההעדפות שלך.

הצג באתר TensorFlow.org הפעל בגוגל קולאב צפה במקור ב-GitHub הורד מחברת

סקירה כללית

במעבדת קוד זה תאמן מודל סיווג תמונה פשוט על מערך הנתונים של CIFAR10, ולאחר מכן תשתמש ב"התקפת מסקנות חברות" כנגד מודל זה כדי להעריך אם התוקף מסוגל "לנחש" אם דוגמה מסוימת הייתה קיימת בערכת האימונים . אתה תשתמש בדוח הפרטיות של TF כדי להמחיש תוצאות ממספר דגמים ונקודות ביקורת מודל.

להכין

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)

התקן את TensorFlow Privacy.

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

הרכבת שני דגמים, עם מדדי פרטיות

סעיף זה רכבות זוג keras.Model מסווג על CIFAR-10 במערך. במהלך תהליך ההכשרה הוא אוסף מדדי פרטיות, שישמשו להפקת דוחות בקטע בקסט.

הצעד הראשון הוא להגדיר כמה הפרמטרים:

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

batch_size=50
epochs_per_report = 2
total_epochs = 50

lr = 0.001

לאחר מכן, טען את מערך הנתונים. אין שום דבר ספציפי לפרטיות בקוד הזה.

Loading the dataset.

לאחר מכן הגדר פונקציה לבניית המודלים.

בנה שני מודלים תלת-שכבתיים של CNN באמצעות הפונקציה הזו.

הגדר את הראשון להשתמש האופטימיזציה SGD בסיסית, והשני להשתמש האופטימיזציה פרטית דיפרנציאלי ( tf_privacy.DPKerasAdamOptimizer ), כך שתוכל להשוות את התוצאות.

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)

הגדר התקשרות חוזרת כדי לאסוף מדדי פרטיות

הבא להגדיר keras.callbacks.Callback כדי periorically לרוץ כמה התקפות פרטיות נגד המודל, והיכנס התוצאות.

Keras fit שיטה תתקשר on_epoch_end השיטה לאחר שכול עידן אימונים. n הטיעון הוא מספר העידן (0 המבוסס).

אתה יכול ליישם הליך זה על ידי כתיבת לולאה כי שוב ושוב קוראת Model.fit(..., epochs=epochs_per_report) ומפעיל את קוד ההתקפה. ההתקשרות חוזרת משמשת כאן רק בגלל שהיא נותנת הפרדה ברורה בין היגיון האימון, והלוגיקת הערכת הפרטיות.

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)

לאמן את הדגמים

בלוק הקוד הבא מאמן את שני הדגמים. all_reports הרשימה משמשת לאיסוף כול תוצאות מכול ריצות האימון המודלים. דיווחי הפרט מתויגים witht model_name , כך שאין בלבול לגבי איזה דגם שנוצר אשר דו"ח.

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

עלילות אפוק

אתה יכול לדמיין כיצד מתרחשים סיכוני פרטיות בזמן שאתה מאמן מודלים על ידי בדיקה של המודל מעת לעת (למשל כל 5 עידנים), אתה יכול לבחור את נקודת הזמן עם הביצועים הטובים ביותר / הפשרה על פרטיות.

השתמש במודול הסקת חברות TF הפרטיות תקפה ליצור AttackResults . אלה AttackResults לקבל בשילוב לתוך AttackResultsCollection . דו"ח פרטיות TF נועד לנתח את מסופק AttackResultsCollection .

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

png

ראה שככלל, פגיעות הפרטיות נוטה לעלות ככל שמספר העידנים עולה. זה נכון לגבי גרסאות דגמים כמו גם סוגי תוקפים שונים.

מודלים דו-שכבתיים (עם פחות שכבות קונבולוציוניות) הם בדרך כלל פגיעים יותר מאשר עמיתיהם לדגמי שלוש השכבות.

כעת נראה כיצד ביצועי המודל משתנים ביחס לסיכון הפרטיות.

פרטיות לעומת שירות

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')

png

דגמי שלוש שכבות (אולי בגלל יותר מדי פרמטרים) משיגים דיוק רכבת של 0.85 בלבד. שני דגמי השכבות משיגים ביצועים שווים בערך לרמה זו של סיכון פרטיות, אך הם ממשיכים לקבל דיוק טוב יותר.

אתה יכול גם לראות כיצד הקו עבור דגמי שתי שכבות נעשה תלול יותר. המשמעות היא שרווחים שוליים נוספים בדייקנות הרכבת באים על חשבון נקודות תורפה עצומות של הפרטיות.

זהו סוף המדריך. אתה מוזמן לנתח את התוצאות שלך.