CAREER: Extending the Foundations of Privacy-Preserving Machine Learning

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Despite their numerous societal benefits, modern machine learning algorithms pose real threats to personal privacy. Differential privacy is a mathematical framework that enables designing learning algorithms with provable privacy guarantees for their input datasets. Despite the recent progress in differentially private (DP) machine learning, our current understanding of the fundamental characteristics of DP learning algorithms is very limited. This career project offers a multifaceted research plan that tackles a broad range of fundamental questions in two important areas of DP machine learning: (i) stochastic optimization and (ii) federated learning. The first is one of the most fundamental tasks in machine learning and the second is one of the most promising applications of modern machine learning. This project aims at: 1) understanding the computational and statistical limits of DP stochastic optimization algorithms, 2) building a comprehensive theory for DP stochastic non-convex optimization, which provides a firm basis for developing new DP algorithms for modern machine learning, and 3) developing new, efficient algorithmic paradigms for DP federated learning that offer meaningful and provable utility guarantees, while taking into account the evolving nature of users’ data and their incentives to participate in collaborative learning. The outcomes of this research are expected to yield the next-generation privacy-preserving learning algorithms that can be implemented for widespread practical use. This career project includes educational and outreach activities such as developing new graduate courses on optimization and differential privacy, and organizing workshops to understand the threats to data privacy in modern machine learning.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.