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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. · 0 citations
#federated learning Conference Sep 2026

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

With the widespread adoption of blockchain technology in domains such as finance, healthcare, and supply chain management, its transparent and immutable ledger mechanism has improved traceability and trustworthiness while simultaneously introducing potential risks of user privacy leakage. Homomorphic encryption (HE), a...

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

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