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PlaidML-HE: Acceleration of Deep Learning Kernels to Compute on Encrypted Data

机译：PlaidML-HE：加速深度学习内核以对加密数据进行计算

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Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.
机译：机器学习即服务（MLaaS）已成为一种流行的做法，服务消费者（例如最终用户）将其数据发送到ML服务并接收预测输出。但是，MLaaS的新兴使用引起了人们对用户专有数据的严重隐私担忧。隐私保护机器学习（PPML）技术旨在将诸如同态加密（HE）和多方计算（MPC）之类的加密原语纳入ML服务中，以从技术角度解决隐私问题。由于现有的PPML解决方案在各种ML前端框架以及硬件后端中假定的高开销和集成难度，因此在实践中尚未得到广泛采用。在这项工作中，我们提出了PlaidML-HE，这是用于PPML推理的第一个端到端HE编译器。利用领域专用语言的功能，PlaidML-HE可以跨多种类型的设备自动生成HE内核。我们评估了PlaidML-HE在不同ML内核上的性能，并证明与现有实现相比，PlaidML-HE大大降低了HE原语的开销。

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