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Comprehensive Privacy Analysis of Deep Learning:!
Passive and Active White-box Inference Attacks against Centralized and Federated Learning !
MiladNasr1,RezaShokri2,AmirHoumansadr1
1University of Massachusetts Amherst, 2National University of Singapore
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Deep learning Tasks!
2
Medical Location Financial Personal History
Privacy Threats!
• We provide a comprehensive privacy analysis of deep learning algorithms.
• Our objective is to measure information leakage of deep learning models about their training data
• In particular we emphasize on membership inference attacks
• Can an adversary infer whether or not a particular data record was part of the training set?
3
Membership Inference!
4
!Trained Model!
!
Output Vector
Member
Non Member
Output !Pattern!
What is the cause of this behavior ? !
Train Data!
Data Distribution
Training a Model!
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0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Train Data
SGD:
𝑾 =𝑾 −𝜶 𝛁𝐋↓𝐰
Model parameters change in the
opposite direction of each training
data point’s gradient!
𝑾 Model parameters 𝐋
Loss 𝛁𝐋↓𝐰 Loss gradient
w.r.t parameters
Training a Model!
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0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Train Data
Training a Model!
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0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Train Data
Non member data
Gradients leak information by
behaving differently for non-member data
vs. member data. !
Gradients Leak Information !
0.4!
0.14!0.1! 0.09! 0.07! 0.07!
0!
0.1!
0.2!
0.3!
0.4!
0.5!
0.6!
0.7!
0.8!
0.9!
1!
0! 100! 200! 300! 400! 500!
Gradient Norm Distribution!
Members!Non-Member!
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Separable distributions
Different Learning/Attack Settings!
• Fully trained
• Black/ White box
• Fine-tuning
• Federated learning
• Central/ local Attacker
• Passive/ Active
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Federated Model!
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training …
Collaborator 1 Collaborator 2 Collaborator 3 Collaborator n
!Central Model!
!
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Federated Learning!
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0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Multiple observations:
Epoch 1 Epoch 2 Epoch n
Every point leave traces on the target function
Active Attack on Federated Learning!
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Target member
Target non-member
0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Active attacker change the
parameters in the direction of the
gradient!
Active Attack on Federated Learning!
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For the data points that are in the
training dataset, local training will
compensate for the active attacker!
0!
0.5!
1!
1.5!
2!
2.5!
3!
3.5!
4!
0! 0.5! 1! 1.5! 2! 2.5! 3! 3.5!
Active Attacks in! Federated Model!
0!
1!
2!
3!
4!
5!
6!
1! 2! 3! 4! 10! 20! 30! 40! 50! 60! 70! 80!
Gra
dien
t nor
m!
Epochs!
Target Members!
Target Non-member!Member instances!
Non-member instances!
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Attacker
Scenario 1: Fully Trained Model!
!!Training
!Trained Model!
!
Dataset
!Trained Model!
!Not Observable
Input Output vector
Cat Dog
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Outputs of all layers
Loss
Gradients of all layers
Scenario 2: Central Attacker in !Federated Model!
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training …
Collaborator 1 Collaborator 2 Collaborator 3 Collaborator n
!Central Model!
!
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Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Collaborator 1 Collaborator 2 Collaborator 3 Collaborator n
!Central Model!
!
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Not observable Not observable Not observable Not observable
Scenario 2: Central Attacker in !Federated Model!
…
Target individual collaborators
Local Updates
!Isolated Model!
!
Isolated any collaborators
In addition to the local attacker observations:
Scenario 3: Local Attacker in !Federated Learning!
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training
Local Data!
!!
LocalModel!
Training …
Collaborator 1 Collaborator 2 Collaborator 3 Collaborator n
!Central Model!
!
Active
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Not Observable Outputs of all layers
Loss
Gradients of all layers
Epoch 1:
Outputs of all layers
Loss
Gradients of all layers
Epoch 2:
.
.
.
Outputs of all layers
Loss
Gradients of all layers
Epoch N:
Score function!
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Outputs of all layers
Loss
Gradients of all layers
Epoch 1:
Outputs of all layers
Loss
Gradients of all layers
Epoch 2:
.
.
.
Outputs of all layers
Loss
Gradients of all layers
Epoch N:
Different observation:
Input! Score ! Member ? Non-member
Experimental Setup!• Unlike previous works, we used publicly available pretrained models • We used all common regularization techniques • We implemented our attacks in PyTorch • We used following datasets: • CIFAR100 • Purchase100 • Texas100
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Results!
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Pretrained Models Attacks!
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Last layer contains the most information
Gradients leak significant information
Federated Attacks!
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Global attack is more powerful than the local attacker
An active attacker can force SGD to leak more information
Conclusions!• We go beyond black-box scenario and try to understand why a deep
learning model leak information
• Gradients leak information about the training dataset
• Attacker in the federated learning can take the advantage of multiple observations to leak more information
• In the federated setting, an attacker can actively force SGD to leak information
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Questions ?
Overall Attack Model!
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Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Layer 1 output!
Layer 1 gradient!
Layer 2 output!
Layer 2 gradient!
Layer n output!
Layer n gradient!
.
.
.
Loss!
Label!
Epoch 1:
Epoch n :
Output Component!
Gradient Component!
Output Component!
Gradient Component!
Output Component!
Gradient Component!
. . .
Loss Component!
Label Component!
Member ? Non-member
Scenario 4: Fine-Tuning Model!
Dataset
!!Training
!General Model!
!
Specialized Dataset
!!Training
!Fine-Tuned
Model!!
Not Observable
Attacker
!General Model!
!
!Fine-Tuned
Model!!
Outputs of all layers
Loss
Gradients of all layers
Outputs of all layers
Loss
Gradients of all layers
General dataset!
Specialized dataset! None!
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Fine-tuning Attacks!
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Both specialized and general datasets are vulnerable to the membership attacks
Dataset Arch
Distinguishing specialized/general
datasets
Distinguishing general / non-member
datasets
Distinguishing Specialized / non-member datasets
Federated Attacks!
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Fine-Tuning Model Leakage!
Dataset
!!Training
!General Model!
!
Specialized Dataset
!!Training
!Fine-Tuned
Model!!
Not Observable
Attacker
!General Model!
!
!Fine-Tuned
Model!!
Outputs of all layers
Loss
Gradients of all layers
Outputs of all layers
Loss
Gradients of all layers
General dataset!
Specialized dataset! None!
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