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cs.CR2026
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…
cs.CR2026
On the Vulnerability of FHE Computation to Silent Data Corruption
Jianan Mu, Ge Yu, Zhaoxuan Kan +6
Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE impl…
cs.CR2025
The Future of Fully Homomorphic Encryption System: from a Storage I/O Perspective
Lei Chen, Erci Xu, Yiming Sun +8
Fully Homomorphic Encryption (FHE) allows computations to be performed on encrypted data, significantly enhancing user privacy. However, the I/O challenges associated with deployin…