3 papers
cs.CR2025
PraxiMLP: A Threshold-based Framework for Efficient Three-Party MLP with Practical Security
Tianle Tao, Shizhao Peng, Haogang Zhu
Efficiency and communication cost remain critical bottlenecks for practical Privacy-Preserving Machine Learning (PPML). Most existing frameworks rely on fixed-point arithmetic for…
cs.CR2025
EVA-S2PLoR: Decentralized Secure 2-party Logistic Regression with A Subtly Hadamard Product Protocol (Full Version)
Tianle Tao, Shizhao Peng, Tianyu Mei +2
The implementation of accurate nonlinear operators (e.g., sigmoid function) on heterogeneous datasets is a key challenge in privacy-preserving machine learning (PPML). Most existin…
cs.CR2024
EVA-S3PC: Efficient, Verifiable, Accurate Secure Matrix Multiplication Protocol Assembly and Its Application in Regression
Shizhao Peng, Tianrui Liu, Tianle Tao +3
Efficient multi-party secure matrix multiplication is crucial for privacy-preserving machine learning, but existing mixed-protocol frameworks often face challenges in balancing sec…