We present Syft, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of a novel privacy-preserving inference information flow where we pass homomorphically encrypted activation signals through a split neural network for inference. We show that splitting the model further up the computation chain significantly reduces the computation time of inference and the payload size of activation signals at the cost of model secrecy. We evaluate our proposed flow with respect to its provision of the core structural transparency principles.

Syft 0.5是一个通用的框架，结合了一组核心隐私增强技术，可实现一组通用的结构化透明度系统。我们通过设计和实现一种新颖的隐私保护推理信息流来演示该框架，其中我们通过拆分神经网络传递同态加密激活信号进行推理。在提供核心结构透明度原则方面，我们评估了我们提出的流程。

Syft 0.5：通用部署的结构化透明度平台