Hengcheng Zhou

Scalable Privacy-Preserving Neural Network Training over Z2k via RMFE-Based Packing and Mixed-Circuit Computation

We introduce a novel framework for privacy-preserving multi-party neural network training over ℤ_(2^k) with semi-honest security in the honest-majority setting. Our work utilizes Shamir secret sharing scheme over Galois rings GR(2^k, d) and is scalable in the number of participants. Our primary cont