arXiv cs.AI· Yang Li, Gang He, Ruichen Zhang, Guangyuan Liu, Yinqiu Liu·· 13 小时前AI 评分31
Pacifo：基于全同态加密的高效轻量级 ResNet，面向可信机器学习即服务
Pacifo: An Efficient Fully Homomorphic Encryption-based Lightweight ResNet for Trustworthy Machine Learning as a Service
AI 导读
Pacifo 是一个基于全同态加密（FHE）的隐私推理框架，通过定制微调策略在加密数据上高效评估 ResNet20。它引入公私层划分方案与高效 ReLU 近似，显著减少私有层数量和昂贵的 FHE 运算，并提供灵活的安全配置。相比现有 ResNet20 的 FHE 实现，Pacifo 运行时加速 3.7-21.7 倍，同时在下游任务上保留原明文模型 95%-99% 的准确率，已开源。
正文 · 原文
Abstract:Machine Learning as a Service (MLaaS) has become a popular paradigm for deploying AI applications. However, it raises significant privacy concerns that the raw client data needs to be uploaded to the server for inference services. Private inference using Fully Homomorphic Encryption (FHE) offers a solution by allowing computations directly on encrypted data, with only the final results decrypted on a client side. However, current practical implementations still suffer from large computational overhead and high latency due to the cost of FHE operations. In this paper, we propose Pacifo, a framework that adapts customized fine-tuning strategies to efficiently evaluate the ResNet20 model in the context of FHE. Specifically, Pacifo introduces a public-private layer partitioning scheme and an efficient ReLU approximation, significantly reducing the number of private layers and costly FHE operations during inference. It also offers flexible security configurations to accommodate varying client privacy requirements. Compared to state-of-the-art FHE implementations of ResNet20, Pacifo achieves a speedup of $3.7-21.7\times$ in runtime while effectively preserving $95\%-99\%$ of the original plaintext model accuracy across downstream tasks. Pacifo is open-sourced at this https URL.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33525 [cs.CR] |
| (or arXiv:2610.33525v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33525 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yang Li [view email]
[v1]
Sat, 24 Oct 2026 08:02:47 UTC (497 KB)
来源：arXiv cs.AI · arxiv.org