Generative Adversarial Models for Learning Private and Fair Representations

Chong Huang, Xiao Chen, Peter Kairouz, Lalitha Sankar, Ram Rajagopal

Abstract: We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data. GAPF leverages recent advances in adversarial learning to allow a data holder to learn "universal" representations that decouple a set of sensitive attributes from the rest of the dataset. Under GAPF, finding the optimal decorrelation scheme is formulated as a constrained minimax game between a generative decorrelator and an adversary. We show that for appropriately chosen adversarial loss functions, GAPF provides privacy guarantees against strong information-theoretic adversaries and enforces demographic parity. We also evaluate the performance of GAPF on multi-dimensional Gaussian mixture models and real datasets, and show how a designer can certify that representations learned under an adversary with a fixed architecture perform well against more complex adversaries.

Keywords: Data Privacy, Fairness, Adversarial Learning, Generative Adversarial Networks, Minimax Games, Information Theory

TL;DR: We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations with certified privacy/fairness guarantees

Data: [HAR](https://paperswithcode.com/dataset/har)