Generative Adversarial Privacy

Understand

We present a data-driven framework called generative adversarial privacy (GAP).

• Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data.
• Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary.
• We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries.