通过软硬件协同设计，降低隐私计算开销，支持大模型规模应用。
Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
- 结合MPC、ZKP与FHE，优化隐私计算性能
- 实现大模型推理等场景的隐私保护运行
- 适合关注隐私安全的大模型落地的开发者
隐私保护技术已推动真实世界系统中可实现的安全计算。其实际应用的主要障碍在于大规模部署时产生的计算与通信开销。本文介绍我们在多方计算（MPC）、零知识证明（ZKPs）和全同态加密（FHE）方面的努力，通过精细的软硬件算法协同设计，推动隐私保护学习系统向实用化迈进。我们展示了在深度神经网络知识产权保护、伦理合规大模型使用控制及Transformer推理等多个场景中的有效性，为实现隐私保护下的大模型应用提供了可行路径。
原文摘要 · Abstract (English)
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
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