@inproceedings{park-etal-2025-powerformer,
title = "Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption",
author = "Park, Dongjin and
Lee, Eunsang and
Lee, Joon-Woo",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.543/",
doi = "10.18653/v1/2025.acl-long.543",
pages = "11090--11111",
ISBN = "979-8-89176-251-0",
abstract = "We propose Powerformer, an efficient homomorphic encryption (HE)-based privacy-preserving language model (PPLM) designed to reduce computation overhead while maintaining model performance. Powerformer incorporates three key techniques to optimize encrypted computations:1. A novel distillation technique that replaces softmax and layer normalization (LN) with computationally efficient power and linear functions, ensuring no performance degradation while enabling seamless encrypted computation.2. A pseudo-sign composite approximation method that accurately approximates GELU and tanh functions with minimal computational overhead.3. A homomorphic matrix multiplication algorithm specifically optimized for Transformer models, enhancing efficiency in encrypted environments.By integrating these techniques, Powerformer based on the BERT-base model achieves a 45{\%} reduction in computation time compared to the state-of-the-art HE-based PPLM without any loss in accuracy."
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<abstract>We propose Powerformer, an efficient homomorphic encryption (HE)-based privacy-preserving language model (PPLM) designed to reduce computation overhead while maintaining model performance. Powerformer incorporates three key techniques to optimize encrypted computations:1. A novel distillation technique that replaces softmax and layer normalization (LN) with computationally efficient power and linear functions, ensuring no performance degradation while enabling seamless encrypted computation.2. A pseudo-sign composite approximation method that accurately approximates GELU and tanh functions with minimal computational overhead.3. A homomorphic matrix multiplication algorithm specifically optimized for Transformer models, enhancing efficiency in encrypted environments.By integrating these techniques, Powerformer based on the BERT-base model achieves a 45% reduction in computation time compared to the state-of-the-art HE-based PPLM without any loss in accuracy.</abstract>
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%0 Conference Proceedings
%T Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption
%A Park, Dongjin
%A Lee, Eunsang
%A Lee, Joon-Woo
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F park-etal-2025-powerformer
%X We propose Powerformer, an efficient homomorphic encryption (HE)-based privacy-preserving language model (PPLM) designed to reduce computation overhead while maintaining model performance. Powerformer incorporates three key techniques to optimize encrypted computations:1. A novel distillation technique that replaces softmax and layer normalization (LN) with computationally efficient power and linear functions, ensuring no performance degradation while enabling seamless encrypted computation.2. A pseudo-sign composite approximation method that accurately approximates GELU and tanh functions with minimal computational overhead.3. A homomorphic matrix multiplication algorithm specifically optimized for Transformer models, enhancing efficiency in encrypted environments.By integrating these techniques, Powerformer based on the BERT-base model achieves a 45% reduction in computation time compared to the state-of-the-art HE-based PPLM without any loss in accuracy.
%R 10.18653/v1/2025.acl-long.543
%U https://aclanthology.org/2025.acl-long.543/
%U https://doi.org/10.18653/v1/2025.acl-long.543
%P 11090-11111
Markdown (Informal)
[Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption](https://aclanthology.org/2025.acl-long.543/) (Park et al., ACL 2025)
ACL