Summary of Secformer: Fast and Accurate Privacy-preserving Inference For Transformer Models Via Smpc, by Jinglong Luo et al.
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
by Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a comprehensive framework called SecFormer to achieve fast and accurate privacy-preserving inference (PPI) for Transformer models. The growing use of these models on cloud platforms raises concerns about the protection of sensitive data, such as investment plans and bank account details. To address this issue, the authors eliminate high-cost exponential and maximum operations in PPI without sacrificing model performance and develop efficient SMPC protocols using suitable numerical computation methods to boost complex nonlinear functions. The framework is shown to outperform MPCFormer in terms of both performance and efficiency, making it a promising solution for PPI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a system that keeps private information safe when using cloud-based Transformer models. It’s a big deal because people might be sharing personal data, like bank accounts or investment plans. The problem is that existing solutions slow down the process too much. The authors developed a new framework called SecFormer to fix this issue. They got rid of some complicated operations and made a new way to do complex math problems quickly and accurately. This system outperforms others in both how well it works and how fast it does its job. |
Keywords
* Artificial intelligence * Inference * Transformer
