Revisiting Differentially Private ReLU Regression Meng Ding Mingxi Lei Liyang Zhu University at Buffalo University at Buffalo KAUST Shaowei Wang Di Wang

As one of the most fundamental non-convex learning problems, ReLU regression under differential privacy (DP) constraints, especially in high-dimensional settings, remains a challenging area in privacy-preserving machine learning.