2025->.md

Privacy-Preserving-Machine-Learning-Resources (2025->)

Content

• Privacy-Preserving-Machine-Learning-Resources
  • Content
• About
• Secure Machine Learning
• Secure Large Language Models

About

This is a current list of resources related to the research and development of privacy-preserving machine learning from 2025 to now.

Secure Machine Learning

• Gibbon: Faster Secure Two-party Training of Gradient Boosting Decision Tree, CCS'2025
• GraphAce: Secure Two-Party Graph Analysis Achieving Communication Efficiency, USENIX Security'2025
• M&M: Secure Two-Party Machine Learning through Modulus Conversion and Mixed-Mode Protocols, TDSC'2025
• Improved Secure Two-party Computation from a Geometric Perspective, USENIX Security'2025

Secure Large Language Models

• Mosformer: Maliciously Secure Three-Party Inference Framework for Large Transformers, CCS'2025
• Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC, USENIX Security'25
• Euston: Efficient and User-Friendly Secure Transformer Inference with Non-Interactivity, S&P'26