Private Minimax

Private minimax optimization focuses on solving minimax problems—where one player aims to minimize while another maximizes—while preserving the privacy of the training data using techniques like differential privacy. Current research emphasizes developing algorithms that achieve optimal trade-offs between privacy guarantees and accuracy, often employing techniques like sparse thresholding and carefully calibrated noise injection within minimax optimization frameworks. This field is crucial for enabling the responsible use of sensitive data in applications like reinforcement learning and generative adversarial networks, where minimax formulations are prevalent, by providing theoretical guarantees on both privacy and generalization performance.