Author

Ling-Ling Ding

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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

#federated learning Conference Sep 2026

Privacy-preserving blockchain systems based on homomorphic encryption: design and challenges

This paper investigates recent advances in the integration of HE with blockchain privacy protection, with particular emphasis on representative schemes employing fully homomorphic encryption (FHE) and partially homomorphic encryption (PHE) in scenarios including privacy-preserving smart contracts, federated learning, a...

Ling-Ling Ding, Bao-Ying Zhang, Yi-Fei Dai et al. · International Conference on... · 0 citations