Shuangyu Cai

Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference

With increasingly serious data privacy concerns and strict regulations, privacy-preserving machine learning (PPML) has emerged to securely execute machine learning tasks without violating privacy. Unfortunately, the computational cost to securely execute nonlinear computations in PPML remains signif…