Author

Meng Li

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Preprint Sep 2026

ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation

ROSETTA is proposed, a hybrid CKKS/TFHE framework that overcomes inefficiency in evaluating nonlinear operations, which incur substantial overhead and dominate the decode stage and achieves up to $4.8\times$ Softmax speedup and $1.5$--$2.1\times$ end-to-end speedup over the SOTA framework CacheMir.

Jiang-Rui Yu, Bao-Sheng Zhang, Liang Kong et al. · 0 citations

#machine learning Preprint Sep 2026

OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

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 cost of significant latency overhead due to HE. Customized HE accelerators have been proposed and have achieved orders-of-magnitude speedup...

Jiang-Rui Yu, Ye Yu, Si Chen et al. · 0 citations