Generative Adversarial Models for Privacy and Fairness
Explore generative adversarial models for privacy and fairness in this 38-minute lecture by Peter Kairouz from Google AI. Delve into zero-sum games, Generative Adversarial Networks (GANs), and their limitations in privacy preservation. Learn about membership inference attacks on GANs and how Differential Privacy (DP) can mitigate these risks. Discover TensorFlow Privacy and differentially private GANs (DP-GAN), including noisy Wasserstein GAN implementations and their results. Investigate context-aware fair data publishing and empirical risk minimization. Examine Generative Adversarial Privacy & Fairness (GAPF) concepts, including data-driven approaches and real-life applications using the GENKI dataset. Understand adversary's neural networks, feedforward and transposed convolution neural network encoders, and the trade-offs between fairness and utility. Explore Siamese-GAPF (S-GAPF) for handling sensitive labels with multiple values, and analyze fairness vs. utility in the Human Activity Recognition (HAR) dataset. Conclude with insights into Gaussian mixture data models for privacy and fairness in machine learning. Read more