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.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
| Editors | Michael B. Matthews |
| Publisher | IEEE Computer Society |
| Pages | 2162-2166 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538692189 |
| DOIs | |
| State | Published - Feb 19 2019 |
| Event | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: Oct 28 2018 → Oct 31 2018 |
Publication series
| Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Volume | 2018-October |
| ISSN (Print) | 1058-6393 |
Conference
| Conference | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
|---|---|
| Country | United States |
| City | Pacific Grove |
| Period | 10/28/18 → 10/31/18 |
Fingerprint
Keywords
- Data Privacy
- Generative Adversarial Networks
- Information Theory
- Minimax Games
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
Cite this
Generative Adversarial Privacy : A Data-Driven Approach to Information-Theoretic Privacy. / Huang, Chong; Kairouz, Peter; Sankar, Lalitha.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2019. p. 2162-2166 8645532 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Generative Adversarial Privacy
T2 - A Data-Driven Approach to Information-Theoretic Privacy
AU - Huang, Chong
AU - Kairouz, Peter
AU - Sankar, Lalitha
PY - 2019/2/19
Y1 - 2019/2/19
N2 - 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.
AB - 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.
KW - Data Privacy
KW - Generative Adversarial Networks
KW - Information Theory
KW - Minimax Games
UR - http://www.scopus.com/inward/record.url?scp=85063003069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063003069&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2018.8645532
DO - 10.1109/ACSSC.2018.8645532
M3 - Conference contribution
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 2162
EP - 2166
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
ER -