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Why not using GANs as they are?
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Classroom Contents
Generative Adversarial Models for Privacy and Fairness
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- 1 Intro
- 2 Zero-sum games
- 3 Generative Adversarial Networks (GANS)
- 4 Why not using GANs as they are?
- 5 Membership inference attacks for GANS
- 6 Differential Privacy (DP) to the rescue!
- 7 TensorFlow Privacy
- 8 Differentially private GANS (DP-GAN)
- 9 DP-GAN: noisy Wasserstein GAN
- 10 DP-GAN results
- 11 Context-aware fair data publishing
- 12 Empirical risk minimization with MI?
- 13 Generative Adversarial Privacy & Fairness (GAPF)
- 14 Example: GAPF under log-loss
- 15 Data-driven GAPF
- 16 Penalty method
- 17 Real-life data: GENKI dataset
- 18 Adversary's neural network
- 19 Feedforward Neural Network (FNN) encoder
- 20 Transposed Convolution Neural Network (TCNNP) encoder
- 21 GENKI fairness vs utility
- 22 Siamese-GAPF (S-GAPF) What if the sensitive label can take many values?
- 23 Real-life data: HAR dataset
- 24 HAR fairness vs utility
- 25 Gaussian mixture data model
