Paper Info
Title
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
Abstract
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet. In this work, we introduce THE-X, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks. THE-X proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and Layer-Norm. Experiments reveal our proposed THE-X can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage.
Year
DOI
Venue
2022
10.18653/v1/2022.findings-acl.277
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022)
DocType
Volume
Citations 
Conference
Findings of the Association for Computational Linguistics: ACL 2022
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Tianyu Chen100.34
Hangbo Bao2183.42
Shaohan Huang35710.29
Li Dong458231.86
Binxing Jiao500.34
Daxin Jiang600.34
Haoyi Zhou714.08
Jianxin Li872592.14
Furu Wei91956107.57