CIF: Small: Collaborative Research: Generative Adversarial Privacy: A Data-driven Approach to Guaranteeing Privacy and Utility

Arizona State University, Scottsdale AZ

Investigators

Abstract

There is a growing need to publish datasets for both public benefit (via data-driven research) and private gains (enterprise data sharing). However, consumer privacy concerns have largely stymied such efforts since large datasets also contain confidential information about participating individuals. This project leverages recent advancements in learning generative models directly from the datasets to introduce a novel framework called generative adversarial privacy (GAP). GAP formalizes adversarial learning as a game between a privatizer that wishes to learn the optimal privacy mechanism and any statistical adversary intent on learning the confidential features. This formalization is crucial to evaluate data-driven approaches against adversaries with strong inferential capabilities. This project will include interactions with Honeywell Labs as well as outreach and dissemination with Stanford industry partners in the electricity and smart cities sector. Outreach programs include exposing middle- and high-school girls to social network privacy challenges at ASU and K-12 teacher training on data science through the Stanford Office of Science Outreach Program. The project will focus on three foundational problems. The first two ensure privacy of confidential features in the published data and involve developing: (i) theoretical limits of the GAP formulation for a large class of loss functions that capture a range of adversarial capabilities; and (ii) convergence guarantees of the proposed GAP model. The third problem focuses on guaranteeing identity privacy via synthetic datasets using a combination of generative models (to generate synthetic data from training data) and classes of statistical adversaries to understand the efficacy of generating synthetic datasets with both utility and privacy guarantees. A key element of this project involves testing on both publicly available datasets as well as proprietary data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.