Hyperion: Private Token Sampling with Homomorphic Encryption
Lawrence Lim, Jiaming Liu, Vikas Kalagi, Divyakant Agrawal University of California, Santa Barbara, Amr El Abbadi University of California, Santa Barbara
A promising direction for enabling private queries to large language models (LLMs) is with homomorphic encryption (HE). An open problem is performing token sampling under HE. In this paper, we introduce Hyperion, an efficient HE algorithm for inverse transform sampling, enabling private token sampling with 1 comparison depth, O(1) amortized comparisons, and O(\log n) rotations. We implement our approach and demonstrate that it samples tokens in 0.14 seconds for 32k tokens (\approx 4.4\mu \mathrm{s} per token) on GPU, achieving a 100\times latency improvement over prior work.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表