Private Transformer Inference in MLaaS: A Survey
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908365362298880 |
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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, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service (MLaaS) raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private Transformer Inference (PTI) offers a solution by utilizing cryptographic techniques such as secure multi-party computation and homomorphic encryption, enabling inference while preserving both user data and model privacy. This paper reviews recent PTI advancements, highlighting state-of-the-art solutions and challenges. We also introduce a structured taxonomy and evaluation framework for PTI, focusing on balancing resource efficiency with privacy and bridging the gap between high-performance inference and data privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Private Transformer Inference in MLaaS: A Survey Li, Yang Zhou, Xinyu Wang, Yitong Qian, Liangxin Zhao, Jun Cryptography and Security Artificial Intelligence Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service (MLaaS) raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private Transformer Inference (PTI) offers a solution by utilizing cryptographic techniques such as secure multi-party computation and homomorphic encryption, enabling inference while preserving both user data and model privacy. This paper reviews recent PTI advancements, highlighting state-of-the-art solutions and challenges. We also introduce a structured taxonomy and evaluation framework for PTI, focusing on balancing resource efficiency with privacy and bridging the gap between high-performance inference and data privacy. |
| title | Private Transformer Inference in MLaaS: A Survey |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10315 |