Security and Privacy Issues in Deep Learning

1. Security and Privacy Issues
in (Deep) Machine Learning

2. An emerging topic
Some early results.
E.g. extracting details of
computer-vision models

3. Some challenges of learning from private data
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Membership attacks
Shokri et al. (2016) Membership Inference Attacks against ML Models
https://arxiv.org/abs/1610.05820
Training-data extraction attacks
Fredrikson et al. (2015) Model Inversion Attacks
https://www.cs.cmu.edu/~mfredrik/papers/fjr2015ccs.pdf
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4. Threat model: black-box
The adversary can query the model.

5. Threat model: white-box
The adversary has the model.

6. }
Quantifying privacy:
Differential Privacy is the gold standard
Randomized
Algorithm
Randomized
Algorithm
Answer 1
Answer 2
...
Answer n
Answer 1
Answer 2
...
Answer n
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7. DEEP LEARNING WITH
DIFFERENTIAL PRIVACY

8. Learning by
Stochastic (Noisy) Gradient Descent
Loss function
worse
better -∇L( 𝜃)

9. Model Training is Inherently Randomized
D
Training Data ModelSGD

10. By Carefully Controlling the Random Noise
We can Ensure Model has Differential Privacy
DD′
Training Data ModelSGD