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.

翻译：我们提出了一个通用框架Syft,这个框架是一个将增进隐私技术核心组合起来的通用框架,它有助于建立一套普遍的结构化透明系统,通过设计和实施一个新的保护隐私的推断信息流动,通过一个分裂的神经网络传送同质加密激活信号,进行推理。我们表明,将模型进一步分解到计算链上会大大缩短推论的计算时间和引爆信号的有效载荷大小,而以模型保密为代价。我们评估了我们关于提供核心结构透明度原则的拟议流程。