3 papers
cs.CR2025
Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC
Tianshi Xu, Wen-jie Lu, Jiangrui Yu +4
This paper presents an efficient framework for private Transformer inference that combines Homomorphic Encryption (HE) and Secure Multi-party Computation (MPC) to protect data priv…
cs.AR2025
Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
Chenqi Lin, Kang Yang, Tianshi Xu +6
With the wide application of machine learning (ML), privacy concerns arise with user data as they may contain sensitive information. Privacy-preserving ML (PPML) based on cryptogra…
cs.CR2024
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Chenqi Lin, Tianshi Xu, Zebin Yang +3
With the fast evolution of large language models (LLMs), privacy concerns with user queries arise as they may contain sensitive information. Private inference based on homomorphic…