3 papers
cs.LG2024
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI
Spencer Giddens, Xiaon Lang, Fang Liu
As data-driven and AI-based decision making gains widespread adoption across disciplines, it is crucial that both data privacy and decision fairness are appropriately addressed. Al…
stat.ML2023
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Spencer Giddens, Fang Liu
Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning m…
stat.ML2023
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3
Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically pro…