Generative Adversarial Models for Privacy and Fairness
Offered By: Simons Institute via YouTube
Course Description
Overview
Syllabus
Intro
Zero-sum games
Generative Adversarial Networks (GANS)
Why not using GANs as they are?
Membership inference attacks for GANS
Differential Privacy (DP) to the rescue!
TensorFlow Privacy
Differentially private GANS (DP-GAN)
DP-GAN: noisy Wasserstein GAN
DP-GAN results
Context-aware fair data publishing
Empirical risk minimization with MI?
Generative Adversarial Privacy & Fairness (GAPF)
Example: GAPF under log-loss
Data-driven GAPF
Penalty method
Real-life data: GENKI dataset
Adversary's neural network
Feedforward Neural Network (FNN) encoder
Transposed Convolution Neural Network (TCNNP) encoder
GENKI fairness vs utility
Siamese-GAPF (S-GAPF) What if the sensitive label can take many values?
Real-life data: HAR dataset
HAR fairness vs utility
Gaussian mixture data model
Taught by
Simons Institute
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