Primer: Fast Private Transformer Inference on Encrypted Data
It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., fully homomorphic encryption (FHE), and multi-party computation (MPC), are popular methods to support private Transformer inference. However, exi...
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| Vydané v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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09.07.2023
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| Abstract | It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., fully homomorphic encryption (FHE), and multi-party computation (MPC), are popular methods to support private Transformer inference. However, existing works still suffer from prohibitively computational and communicational overhead. In this work, we present, Primer, to enable a fast and accurate Transformer over encrypted data for natural language processing tasks. In particular, Primer is constructed by a hybrid cryptographic protocol optimized for attention-based Transformer models, as well as techniques including computation merge and tokens-first ciphertext packing. Comprehensive experiments on encrypted language modeling show that Primer achieves state-of-the-art accuracy and reduces the inference latency by 90.6% ∼ 97.5% over previous methods. |
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| AbstractList | It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., fully homomorphic encryption (FHE), and multi-party computation (MPC), are popular methods to support private Transformer inference. However, existing works still suffer from prohibitively computational and communicational overhead. In this work, we present, Primer, to enable a fast and accurate Transformer over encrypted data for natural language processing tasks. In particular, Primer is constructed by a hybrid cryptographic protocol optimized for attention-based Transformer models, as well as techniques including computation merge and tokens-first ciphertext packing. Comprehensive experiments on encrypted language modeling show that Primer achieves state-of-the-art accuracy and reduces the inference latency by 90.6% ∼ 97.5% over previous methods. |
| Author | Lou, Qian Zheng, Mengxin Jiang, Lei |
| Author_xml | – sequence: 1 givenname: Mengxin surname: Zheng fullname: Zheng, Mengxin email: zhengme@iu.edu organization: Indiana University Bloomington – sequence: 2 givenname: Qian surname: Lou fullname: Lou, Qian email: qian.lou@ucf.edu organization: University of Central Florida – sequence: 3 givenname: Lei surname: Jiang fullname: Jiang, Lei email: jiang60@iu.edu organization: Indiana University Bloomington |
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| Snippet | It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g.,... |
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| SubjectTerms | Computational modeling Cryptographic Protocol Cryptography Design automation Fully Homomorphic Encryption Multi-party computation Natural language processing Private Inference Solids Transformer Transformers |
| Title | Primer: Fast Private Transformer Inference on Encrypted Data |
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