# Generative Adversarial Privacy

arXiv: https://arxiv.org/abs/1807.05306 · HTML: https://mlsanity.com/paper/1807.05306/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Privacy-Preserving Action Recognition via Motion Difference Quantization](https://mlsanity.com/paper/2208.02459/) (2022)
- [Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility](https://mlsanity.com/paper/2207.04895/) (2022)
- [Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation](https://mlsanity.com/paper/2205.01950/) (2022)
- [Decouple-and-Sample: Protecting sensitive information in task agnostic data release](https://mlsanity.com/paper/2203.13204/) (2022)
- [Privacy-Preserving Image Acquisition Using Trainable Optical Kernel](https://mlsanity.com/paper/2106.14577/) (2021)
- [Lossy Compression of Noisy Data for Private and Data-Efficient Learning](https://mlsanity.com/paper/2202.02892/) (2022)
- [Machines & Influence: An Information Systems Lens](https://mlsanity.com/paper/2111.13365/) (2021)
- [Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization](https://mlsanity.com/paper/2404.05043/) (2024)

## Abstract (arXiv)

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. We also evaluate GAP's performance on the GENKI face database.