Author

Jiang-Rui Yu

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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. · 1 citation

#machine learning Preprint Sep 2026

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

OptiPrime is introduced, a protocol-hardware co-optimization framework for efficient private DNN inference that features a novel HE protocol for convolutions that substantially reduces the number of transmitted output ciphertexts and mitigates the network communication bottleneck.

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