113 citations · 156 across the 6 of their papers we have counts for
6 papers
Hidden Technical Debts for Fair Machine Learning in Financial Services
Chong Huang, Arash Nourian, Kevin Griest
The recent advancements in machine learning (ML) have demonstrated the potential for providing a powerful solution to build complex prediction systems in a short time. However, in…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…
Generative Adversarial Privacy
Chong Huang, Peter Kairouz, Xiao Chen +2
We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holde…
Context-Aware Generative Adversarial Privacy
Chong Huang, Peter Kairouz, Xiao Chen +2
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions,…
Market Segmentation for Privacy Differentiated "Free" Services
Chong Huang, Lalitha Sankar
The emerging marketplace for online free services in which service providers earn revenue from using consumer data in direct and indirect ways has lead to significant privacy conce…
Designing Incentive Schemes For Privacy-Sensitive Users
Chong Huang, Lalitha Sankar, Anand D. Sarwate
Businesses (retailers) often wish to offer personalized advertisements (coupons) to individuals (consumers), but run the risk of strong reactions from consumers who want a customiz…