论文标题
直升机：用于加密数据的大型神经网络的瓷砖张量框架
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data
论文作者
论文摘要
隐私权解决方案使公司能够在履行政府法规的同时将机密数据卸载到第三方服务。为此，他们利用各种加密技术（例如同构加密）（HE），允许对加密数据进行计算。大多数他的计划都以SIMD方式进行，并且数据包装方法可以极大地影响运行时间和内存成本。找到导致最佳性能实现的包装方法是一项艰巨的任务。 我们提出了一个简单而直观的框架，该框架将用户的包装决定抽象。我们解释其基本数据结构和优化器，并提出了一种用于执行2D卷积操作的新算法。我们使用此框架来实现Alexnet的HE友好版本，该版本在三分钟内运行，比仅使用HE的其他最先进的解决方案快几个数量级。
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.
