John Stephan

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

2021 · arXiv (Cornell University)

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both …

Balancing Privacy, Robustness, and Efficiency in Machine Learning

2023 · arXiv (Cornell University)

This position paper argues that achieving robustness, privacy, and efficiency simultaneously in machine learning systems is infeasible under prevailing threat models. The tension between these goals arises not from algorithmic shortcomings but from structural limitations …