4 papers
ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation
Jiangrui Yu, Baosheng Zhang, Liang Kong +5
Generative large language models (LLMs) have achieved state-of-the-art performance on many real-world tasks such as code generation and question answering. These models predominant…
OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
Jiangrui Yu, Ye Yu, Si Chen +5
Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the…
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…
Trinity: A General Purpose FHE Accelerator
Xianglong Deng, Shengyu Fan, Zhicheng Hu +9
In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single…