A Survey on Private Transformer Inference
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909424556179456 |
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| author | Li, Yang Zhou, Xinyu Wang, Yitong Qian, Liangxin Zhao, Jun |
| author_facet | Li, Yang Zhou, Xinyu Wang, Yitong Qian, Liangxin Zhao, Jun |
| contents | Transformer models have revolutionized AI, enabling applications like content generation and sentiment analysis. However, their use in Machine Learning as a Service (MLaaS) raises significant privacy concerns, as centralized servers process sensitive user data. Private Transformer Inference (PTI) addresses these issues using cryptographic techniques such as Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE), enabling secure model inference without exposing inputs or models. This paper reviews recent advancements in PTI, analyzing state-of-the-art solutions, their challenges, and potential improvements. We also propose evaluation guidelines to assess resource efficiency and privacy guarantees, aiming to bridge the gap between high-performance inference and data privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Private Transformer Inference Li, Yang Zhou, Xinyu Wang, Yitong Qian, Liangxin Zhao, Jun Cryptography and Security Artificial Intelligence Transformer models have revolutionized AI, enabling applications like content generation and sentiment analysis. However, their use in Machine Learning as a Service (MLaaS) raises significant privacy concerns, as centralized servers process sensitive user data. Private Transformer Inference (PTI) addresses these issues using cryptographic techniques such as Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE), enabling secure model inference without exposing inputs or models. This paper reviews recent advancements in PTI, analyzing state-of-the-art solutions, their challenges, and potential improvements. We also propose evaluation guidelines to assess resource efficiency and privacy guarantees, aiming to bridge the gap between high-performance inference and data privacy. |
| title | A Survey on Private Transformer Inference |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08145 |