Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911035650211840 |
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| author | Chen, Yuntian Tang, Zhanyong Lu, Tianpei Zhang, Bingsheng Shi, Zhiying Wang, Zheng |
| author_facet | Chen, Yuntian Tang, Zhanyong Lu, Tianpei Zhang, Bingsheng Shi, Zhiying Wang, Zheng |
| contents | Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensitive sectors like medicine and finance. However, private TBM inference incurs significant costs due to the coarse-grained application of HE and SS. We present FASTLMPI, a new approach to accelerate private TBM inference through fine-grained computation optimization. Specifically, through the fine-grained co-design of homomorphic encryption and secret sharing, FASTLMPI achieves efficient protocols for matrix multiplication, SoftMax, LayerNorm, and GeLU. In addition, FASTLMPI introduces a precise segmented approximation technique for differentiable non-linear, improving its fitting accuracy while maintaining a low polynomial degree. Compared to solution BOLT (S&P'24), FASTLMPI shows a remarkable 54% to 64% decrease in runtime and an impressive 72.2% reduction in communication costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation Chen, Yuntian Tang, Zhanyong Lu, Tianpei Zhang, Bingsheng Shi, Zhiying Wang, Zheng Cryptography and Security Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensitive sectors like medicine and finance. However, private TBM inference incurs significant costs due to the coarse-grained application of HE and SS. We present FASTLMPI, a new approach to accelerate private TBM inference through fine-grained computation optimization. Specifically, through the fine-grained co-design of homomorphic encryption and secret sharing, FASTLMPI achieves efficient protocols for matrix multiplication, SoftMax, LayerNorm, and GeLU. In addition, FASTLMPI introduces a precise segmented approximation technique for differentiable non-linear, improving its fitting accuracy while maintaining a low polynomial degree. Compared to solution BOLT (S&P'24), FASTLMPI shows a remarkable 54% to 64% decrease in runtime and an impressive 72.2% reduction in communication costs. |
| title | Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2412.16537 |