Generative Adversarial Models for Learning Private and Fair Representations

We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations. GAPF leverages recent advancements 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 privacy mechanism is formulated as a constrained minimax game between a private/fair encoder 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.