Secure Transformer-Based Neural Network Inference for Protein Sequence Classification

Jingwei Chen; Linhan Yang; Chen Yang; Shuai Wang; Rui Li; Weijie Miao; Wenyuan Wu; Li Yang; Kang Wu; Lizhong Dai

Paper 2024/1851

Secure Transformer-Based Neural Network Inference for Protein Sequence Classification

Jingwei Chen, CIGIT, CAS

Linhan Yang, Chongqing Jiaotong University

Chen Yang, CIGIT, CAS

Shuai Wang, Chongqing Jiaotong University

Rui Li, CIGIT, CAS

Weijie Miao, CIGIT, CAS

Wenyuan Wu, CIGIT, CAS

Li Yang, Sansure Biotech, Inc.

Kang Wu, Sansure Biotech, Inc.

Lizhong Dai, Sansure Biotech, Inc.

Abstract

Protein sequence classification is crucial in many research areas, such as predicting protein structures and discovering new protein functions. Leveraging large language models (LLMs) is greatly promising to enhance our ability to tackle protein sequence classification problems; however, the accompanying privacy issues are becoming increasingly prominent. In this paper, we present a privacy-preserving, non-interactive, efficient, and accurate protocol called encrypted DASHformer to evaluate a transformer-based neural network for protein sequence classification named DASHformer, provided by the iDASH 2024-Track 1 competition. The presented protocol is based on our solution for this competition, which won the first place. It is arguably the first secure transformer inference protocol capable of performing batch classification for multiple protein sequences in a single execution only using leveled homomorphic encryption (i.e., without bootstrapping). To achieve this, we propose a series of new techniques and algorithmic improvements, including data-driven non-polynomial function fitting, tensor packing, and double baby-step-giant-step for computing the product of multiple encrypted matrices. These techniques and improvements enable the protocol to classify $163$ encrypted protein sequences in about $165$ seconds with $128$-bit security, achieving an amortized time of about one second per sequence.

Note: Solution for iDASH 2024-Track 1 from SCB@Chongqing

Metadata

Available format(s): PDF
Category: Applications
Publication info: Preprint.
Keywords: Homomorphic Encryption LLMs Privacy-Preserving Computing Protein Classification Transformer
Contact author(s): chenjingwei @ cigit ac cn
History: 2024-11-15: approved; 2024-11-12: received; See all versions
Short URL: https://ia.cr/2024/1851
License: CC BY-NC

BibTeX

@misc{cryptoeprint:2024/1851,
      author = {Jingwei Chen and Linhan Yang and Chen Yang and Shuai Wang and Rui Li and Weijie Miao and Wenyuan Wu and Li Yang and Kang Wu and Lizhong Dai},
      title = {Secure Transformer-Based Neural Network Inference for Protein Sequence Classification},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/1851},
      year = {2024},
      url = {https://eprint.iacr.org/2024/1851}
}