@inproceedings{55e7edb9fbc7485a9a1de576142e73e6,
title = "Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy",
abstract = "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.",
keywords = "Data Privacy, Generative Adversarial Networks, Information Theory, Minimax Games",
author = "Chong Huang and Peter Kairouz and Lalitha Sankar",
note = "Funding Information: This work is supported in part by the National Science Foundation under Grant No. CAREER Award CCF-1350914 and CIF-1815361. Publisher Copyright: {\textcopyright} 2018 IEEE.; 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2019",
month = feb,
day = "19",
doi = "10.1109/ACSSC.2018.8645532",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "2162--2166",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
}