Hanlin Zhang

Efficient Verifiable Computation for Support Vector Machine Training over Secret-Shared Data

A lightweight, privacy-preserving, and verifiable SVM training scheme designed for resource-constrained clients, which maintains stable classification performance while achieving an order-of-magnitude decrease in training runtime compared with existing ciphertext-based methods.