保护用户数据隐私的Transformer推理技术综述
Private Transformer Inference in MLaaS: A Survey
- 用加密技术实现私有化Transformer推理
- 兼顾效率与隐私，提升模型部署安全性
- 适合关注隐私计算的算法工程师
Transformer模型推动了AI发展，广泛应用于内容生成和情感分析。但在机器学习即服务（MLaaS）场景中，集中式处理敏感用户数据引发严重隐私问题。私有Transformer推理（PTI）通过安全多方计算和同态加密等密码学技术，在保护用户数据和模型隐私的前提下完成推理。本文综述了近期PTI进展，提出结构化分类体系与评估框架，重点解决高性能推理与数据隐私之间的平衡难题。
原文摘要 · Abstract (English)
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.
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