Secure Transformer Inference

Mu Yuan; Lan Zhang; Xiang-Yang Li

Paper 2023/1763

Secure Transformer Inference

Mu Yuan

, University of Science and Technology of China

Lan Zhang, University of Science and Technology of China

Xiang-Yang Li, University of Science and Technology of China

Abstract

We present a three-party protocol that can protect both Transformer parameters and user data during the inference phase. For each feedforward inference process, our protocol only introduces permutation computation of input and output data on the user side. Our protocol, Secure Transformer Inference Protocol (STIP), can be applied to real-world services like ChatGPT.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Preprint.
Keywords: large language model transformer inference secure protocol
Contact author(s): ym0813 @ mail ustc edu cn
zhanglan @ ustc edu cn
xiangyangli @ ustc edu cn
History: 2023-11-17: approved; 2023-11-15: received; See all versions
Short URL: https://ia.cr/2023/1763
License: CC BY

BibTeX

@misc{cryptoeprint:2023/1763,
      author = {Mu Yuan and Lan Zhang and Xiang-Yang Li},
      title = {Secure Transformer Inference},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1763},
      year = {2023},
      note = {\url{https://eprint.iacr.org/2023/1763}},
      url = {https://eprint.iacr.org/2023/1763}
}