Paper 2025/2318 Original: eprint.iacr.org/2025/2318 ↗
Hyperion: Private Token Sampling with Homomorphic Encryption
Abstract
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.
Metadata
- Available format(s)
PDF
- Category
- Applications
- Publication info
- Preprint.
- History
- 2026-07-03: last updated
- 2025-12-23: published
- Short URL
- https://ia.cr/2025/2318
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2025/2318,
author = {Lawrence Lim and Jiaming Liu and Vikas Kalagi and Divyakant Agrawal and Amr El Abbadi},
title = {Hyperion: Private Token Sampling with Homomorphic Encryption},
howpublished = {Cryptology {ePrint} Archive, Paper 2025/2318},
year = {2025},
url = {https://eprint.iacr.org/2025/2318}
}