Paper 2026/491

SoK: Private Transformer-Based Model Inference

Tianpei Lu, Zhejiang University

Zhanyong Tang, Northwest University

Bingsheng Zhang, Zhejiang University

Zhiying Shi, Northwest University

Yuxiang Luan, Northwest University

Zhuzhu Wang, Northwest University

Abstract

The growing demand for privacy-preserving Transformer inference has led to the emergence of numerous protocols designed to protect sensitive data and model parameters. These protocols utilize diverse cryptographic tools under varying assumptions, each presenting unique characteristics and trade-offs between computation, communication, and accuracy. In this paper, we conduct a systematic and in-depth analysis of existing approaches from diverse performance perspectives, identifying their limitations and research gaps. We further evaluate the reproducibility of prior systems and re-benchmark representative solutions under standardized configurations. Our results yield a principled guideline for balancing protocol trade-offs under different deployment settings.

Note: Update intro and Table in appendix

@misc{cryptoeprint:2026/491,
      author = {Yuntian Chen and Tianpei Lu and Zhanyong Tang and Bingsheng Zhang and Zhiying Shi and Yuxiang Luan and Zhuzhu Wang},
      title = {{SoK}: Private Transformer-Based Model Inference},
      howpublished = {Cryptology {ePrint} Archive, Paper 2026/491},
      year = {2026},
      url = {https://eprint.iacr.org/2026/491}
}