HeLayers：面向加密数据上大型神经网络的瓦片张量框架
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data
AI总结：
针对同态加密（HE）下数据打包难影响性能的问题，提出HeLayers瓦片张量框架，抽象打包决策并设计新型二维卷积算法，实现的HE友好型AlexNet比同类方案快数个数量级。
摘要：
隐私保护解决方案使企业能够在满足政府法规的前提下，将机密数据卸载给第三方服务。为实现这一目标，它们利用同态加密（HE）等多种密码技术，该技术支持对加密数据执行计算。大多数HE方案以单指令多数据（SIMD）方式运行，数据打包方法会显著影响运行时间和内存开销。找到能实现最优性能的打包方法是一项难题。\n我们提出了一个简单直观的框架，为用户抽象了打包决策。我们阐释了其底层数据结构和优化器，并提出了一种执行二维卷积运算的新算法。我们使用该框架实现了适配HE的AlexNet版本，其运行时间为三分钟，比其他仅使用HE的前沿解决方案快数个数量级。
英文摘要：
Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
