Homomorphic encryption (HE)--the ability to perform computations on encrypted data--is an attractive remedy to increasing concerns about data privacy in the field of machine learning. However, building models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in deep learning, cryptography, and software engineering. Deep learning frameworks, together with recent advances in graph compilers, have greatly accelerated the training and deployment of deep learning models to a variety of computing platforms. Here, we introduce nGraph-HE, an extension of the nGraph deep learning compiler, which allows data scientists to deploy trained models with popular frameworks like TensorFlow, MXNet and PyTorch directly, while simply treating HE as another hardware target. This combination of frameworks and graph compilers greatly simplifies the development of privacy-preserving machine learning systems, provides a clean abstraction barrier between deep learning and HE, allows HE libraries to exploit HE-specific graph optimizations, and comes at a low cost in runtime overhead versus native HE operations.
翻译:以加密数据加密(HE) — — 计算加密数据的能力 — — 是提高机器学习领域数据隐私关注度的一个有吸引力的补救办法。然而,在加密文本上建立运作模型目前是劳动密集型的,需要同时具备深层学习、加密和软件工程方面的专业知识。 深层次学习框架,加上最近在图形编纂器方面的进步,大大加快了在各种计算机平台上培训和部署深层学习模型的速度。 在这里,我们引入了nGraph-HE,这是NGraph深层学习汇编器的延伸,它使数据科学家能够直接利用诸如TensorFlow、MXNet和PyTorrch等广受欢迎的框架来部署经过培训的模型,而只是将HE作为另一个硬件目标。 这种框架和图形编纂器的组合极大地简化了隐私保存机器学习系统的发展,为深层学习和HE提供了干净的抽象屏障,使He图书馆能够利用特定图形优化,并且以低廉的运行成本相对于当地HE业务。