Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy

Chong Huang; Peter Kairouz; Lalitha Sankar

doi:10.1109/ACSSC.2018.8645532

Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy

Chong Huang, Peter Kairouz, Lalitha Sankar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/ACSSC.2018.8645532
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

Huang, C., Kairouz, P., & Sankar, L. (2019). Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 2162-2166). [8645532] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645532

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

Huang, C, Kairouz, P & Sankar, L 2019, Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645532, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 2162-2166, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 10/28/18. https://doi.org/10.1109/ACSSC.2018.8645532

Huang C, Kairouz P, Sankar L. Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2019. p. 2162-2166. 8645532. (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2018.8645532

Huang, Chong ; Kairouz, Peter ; Sankar, Lalitha. / Generative Adversarial Privacy : A Data-Driven Approach to Information-Theoretic Privacy. Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. editor / Michael B. Matthews. IEEE Computer Society, 2019. pp. 2162-2166 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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Access to Document

10.1109/ACSSC.2018.8645532